TER 3 & S e - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/7336/15/15_chapter 3.pdf[In this chapter, the researcher has ... (Malhotra, 2004). The test ... The researcher had

[In this chapter, the researcher has outlined the

various data reduction as well as data analysis

tools used in the research. The researcher has

shown a blue print of data analysis plan. Various

statistical techniques like Factor Analysis,

Discriminant Analysis, Correlation and

Regression, Multi-Dimensional Scaling and

Attribute Based Perceptual Mapping, Cross

Tabulation using Chi-Square, ANOVAs and the

Design of Experiments, etc. have been used.

Later in the chapter the researcher has tried to

find the answers for predefined research

objectives, research questions and has tested the

research hypothesis using above mentioned

statistical tools.]

Chapter 3 Outline: 3.1: Plan of Data

Analysis

3.2: Major Findings

3.3: Hypothesis Testing

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CHAPTER 3: DATA ANALYSIS & INTERPRETATIONS

Brand Management in Banking Industry with Special Focus on Corporate Branding of Banks:

A Case Study of Corporate Banks Operating in Gujarat State Pag

e184

Chapter 3. Data Analysis and Interpretations

In this chapter, the researcher has outlined various data reduction as well as data

analysis tools used in the research. The researcher has shown a blue print of data

analysis plan. Various statistical techniques like Factor Analysis, Discriminant

Analysis, Correlation and Regression, Multi-Dimensional Scaling and Attribute

Based Perceptual Mapping, Cross Tabulation using Chi-Square, ANOVAs and the

Design of Experiments, etc. have been used for data analysis & interpretations.

Later in the chapter the researcher has tried to find the answers for predefined

research objectives and research questions. The researcher has also tested the

research hypotheses using above mentioned statistical tools.

3.1: Plan of Data Analysis

The Researcher has used various statistical tools and techniques to analyze and

interpret the data, some of the tools used for data analysis and their usefulness are

as below:

3.1.1: Reliability of scale

Mainly, reliability is a measure of how a scale can be relied on to produce

similar measurements every time we use the scale. SPSS offers the use of

reliability, like Cronbach’s alpha (Nargundkar, 2008). To perform

Reliability analysis of a scale that consists of various items/variables were

selected accordingly. Then alpha was used as the model followed by

descriptive for scale and descriptive for scale if item deleted. The researcher

also used Inter-item correlation.

If the alpha value for the scale is 0.7 or more, it is usually considered a good

scale (Nargundkar, 2008 & Malhotra, 2004). If the item to total correlation is

low for an item, we can consider dropping the item form the scale. We can

also take this decision based on a look at Alpha value after dropping an item.

If the alpha value is high even after dropping a particular item, we can drop

the item.




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3.1.2: Factor Analysis

Factor analysis is a very useful method of reducing data complexity by

reducing the number of variables being studied. It is a common experience,

for example, to find a marketing decision maker wondering what exactly

makes a consumer buy this product (Malhotra, 2004). The possible purchase

criteria could range from just one or two of fifteen or twenty, and often, the

marketing manager is shooting in the significant dark, trying to figure out

what really drives buyer behavior. In a more general ways factor analysis is a

set of techniques which, by analyzing correlations between variables, reduces

their number into fewer factors which explain much of the original data, more

economically. Other creative uses of factor analysis could be to do it

separately for 2 groups such as users and non-users of a brand, and check what

differences exist in the factors extracted (Nargundkar, 2008). In determining

the relative power of each discriminant variable, discriminant loadings are

considered to be more valid and in general any variable/factor with loadings

of 0.30 or higher is considered to be statistically significant (Adapa, 2008).

There are two stages in factor analysis. Stage 1 can be called the Factor

Extraction process, where our objective is to identify how many factors will

be extracted from the data. The most popular method for this is called

Principal Component Analysis. Stage 2 is called Rotation of principal

components. This is actually optional, but highly recommended. After the

number of extracted factors is decided upon in stage 1, the next task of the

researcher is to interpret and name the factors. This is similar to correlation

matrix, with loadings having values between 0 and 1. Values close to 1

represent high loadings and those close to 0, low loadings. There are two

popular methods of orthogonal rotation, the varimax and the quartimax

(Nargundkar, 2008).

Kaiser or Eigenvalue specification is one of the most popular methods used

for determining the number of factors to be extracted. The method suggests

the Eigenvalues greater than one should be retained. The Scree-test is also

performed for determining the number of factors. The Scree-test advocates




e186

that factoring process needs to be stopped at a point where the eigenvalues

begins to level-off forming a straight line with an almost horizontal slope

(Sukanya and Shilpa, 2009).

3.1.3: Discriminant Analysis

The major application are for this technique is where we want to be able to

distinguish between two or three sets of objects or people, based on the

knowledge of some of their characteristics (Nargundkar, 2008). To

summarize, we can use linear discriminant analysis when we have to classify

objects into two or more groups based on the knowledge of some variables

(characteristics) related to them. Typically these groups would be user/no-user

or loyal/non-loyal, satisfied/non-satisfied or on similar lines.

3.1.4: Correlation and Regression Analysis

Correlation and Regression are generally performed together. The application

of correlation analysis is to measure the degree of association between two

sets of quantitative data. For example, how are sales of product A correlated

with sales of product B? Or how is the advertising expenditure correlated with

other promotional expenditure? Correlation is usually followed by regression

analysis in many applications. The main objective of regression is to explain

the variation in one variable (called the dependent variable), based on the

variation in one or more other variables (called the impendent variables). The

application areas are in „explaining‟ variations in sales of a product based on

advertising expenses, or number of sales people, or number of sales offices, or

on all the above variables.

There are basically two approaches to regression (Nargundkar, 2004):

A. A hit-and-trail approach

Y= a + b1x1 + b2x2 + … + bnxn

Where y is the dependent variable and x1, x2, x3… xn, are the independent

variables expected to be related to y.

B. A pre-conceived approach.




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The output consists of b coefficients for all the independent variables in the

model. The output also gives you the results of a t-test for the significance

of each variable in the model, and the results of the F-test for the model on

the whole.

Assuming the model is statistically significant at the desired confidence

level (usually 90 or 95% for typical application in the marketing area), the

coefficient of determination or R-Square of the model is an important part

of the output. The R-Square value is the percentage (or proportion) of the

total variance in Y explain by all the impendent variables in the regression

equation.

3.1.5: Multi-Dimensional Scaling and Attribute based

perceptual mapping

The most common and useful marketing application of multidimensional

scaling is in product positioning or brand positioning. One obvious way to do

that is to ask customers what they think of competing brands on say, six

attributes with a rating scale of 1 to 5 points. This would result in ratings for

all brands on all attributes, which could be taken two attributes at a time, and

plotted on a graph. There are two basic methods used in multidimensional

scaling (Nargundkar, 2008):

A. Attribute based approach

B. Similarity/Dissimilarity based approach.

The number of dimensions is decided upon by a measure known as „stress‟

which is given by the computer output for a solution in each dimension. The

objective should be to get the solution with an acceptable low value of stress

(indicating a good fit, because stress is measure of lack-of-fit), in the least

possible number of dimensions. Once the numbers of dimensions are decided

on, the solution for that dimensionality should be looked at, and the objects

(brands) plotted on a map if possible, for visual clarity. Then come the

difficult part, of naming the dimensions. This has to be done keeping in mind




e188

the attributes of the brands, their target segments by age, price, quality, or

attempted positioning through brand communication, and so on.

3.1.6: Cross Tabulation using Chi-square

In the case of cross-tabulation featuring two variables, a test of significance

called the Chi-Squared test can be used to test if the two variables are

statistically associated with each other significantly (Malhotra, 2004). The test

is mainly used to find the correlation between two variables namely dependent

and independent.

Say for example „Brand Loyalty‟ and the „Staff Response‟ in banks are

correlated or not.

3.1.7: ANOVA and the Design of Experiments

The application areas for experiments in marketing research are wide.

Whenever a marketing mix variable (independent variable) such as price, a

specific promotion, or type of distribution, even specific elements like shelf

space, or color of packaging, and so on is changed, we would want to know its

effect.

A one-independent variable experiment is called one-way ANOVA. ANOVA

stands for Analysis of Variance, the generic name given to a set of techniques

for studying the cause-and-effect of one or more factors on a single dependent

variable.

The major types of ANOVA design are (Nargundkar, 2004):

A. Completely Randomized Design in a One-Way ANOVA (single factor)

B. Randomized Block Design (single blocking factor)

C. Latin Square Design

D. Factorial Design with 2 or more factors.




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3.2: Major Findings

The researcher had survey total 1050 banking customers and 35 bank branch

managers of pre-specified seven bank brands from six major cities of Gujarat,

namely Ahmadabad, Vadodara, Surat, Rajkot, Jamnagar and Bhavnagar. All the

data collected was coded properly and were entered in to the Microsoft EXCEL.

Proper data reduction tools like factor analysis, discriminant analysis, etc. were

used to reduce the number of variables from the total data. Let‟s go through the

major finding of the research.

3.2.1: Customers’ Profile

The researcher has surveyed total 1050 customers of pre-specified seven banks,

namely AXIS, ICICI, HDFC, CITI, HSBC, BOB and SBI(refer Table 3.1(A)).

Most of the customers are having different income, education, and professional

background, which represents well distributed sample. Let us study the each

demographic factor of customers one by one.

3.2.1.1: Customers’ Bank and Location

Looking at Table 3.1(A), we can see that Ahmadabad city has 50 customers

from each bank, where as all other city consist of 25 customers from each

bank. The banks like CITI and HSBC are operating only in Metro Cities due to

which it was not possible to collect the responses of its customers from other

cities.

Table 3.1(A): Customer’s Bank Name and City

City/Bank

Name

AXIS HDFC ICICI CITI HSBC BOB SBI Total

Count Count Count Count Count Count Count Count

AHMEDABAD 50 50 50 50 50 50 50 350

VADODARA 25 25 25 25 25 25 25 175

SURAT 25 25 25 25 0 25 25 150

RAJKOT 25 25 25 0 0 25 25 125

BHAVNAGAR 25 25 25 0 0 25 25 125

JAMNAGAR 25 25 25 0 0 25 25 125

Total 175 175 175 100 75 175 175 1050




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Chart 3.1(A): Customer’s Bank Name and City

3.2.1.2: Customers’ Gender and Bank Brand

Looking at Table 3.1(B), we can see that 77.61 percent of the respondents were

male and remaining 22.38 percent of the respondents were female. As female

are less involved in banking in Gujarat it was difficult to collect responses

from them.

Table 3.1(B): Customer’s Gender and Bank Brand

Gender/Bank

Name



Male 136 154 131 90 56 112 136 815

Female 39 21 44 10 19 63 39 235

Total 175 175 175 100 75 175 175 1050

Chart 3.1(B): Customer’s Gender and Bank Brand




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3.2.1.3: Customers’ Age and Bank Brand

Looking at Table 3.1(C), we can see that 70 percent of the respondents belong

to age group of 21-30, and 18% belongs to 31-40 years.

Table 3.1(C): Customer’s Age and Bank Brand

Bank

Name/

Age

10-20 21-30 31-40 41-50

More

Than

50 Years

Total

C N% C N% C N% C N% C N% C N%

AXIS 4 2% 126 72% 32 18% 11 6% 2 1% 175 100%

HDFC 2 1% 141 81% 19 11% 11 6% 2 1% 175 100%

ICICI 1 1% 143 82% 21 12% 7 4% 3 2% 175 100%

CITI 7 7% 38 38% 38 38% 15 15% 2 2% 100 100%

HSBC 3 4% 41 55% 16 21% 10 13% 5 7% 75 100%

BOB 0 0% 123 70% 30 17% 14 8% 8 5% 175 100%

SBI 2 1% 120 69% 34 19% 12 7% 7 4% 175 100%

Total 19 2% 732 70% 190 18% 80 8% 29 3% 1050 100%

Chart 3.1(C): Customer’s Age and Bank Brand

3.2.1.4: Customers’ Education and Bank Brand

Looking at Table 3.1(D), we can see that 46 percent of the respondents were

Graduate and 40 percent were Post Graduate.




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Table 3.1(D): Customer’s Education and Bank Brand

Bank/

Education

SSC HSC DIPLOMA GRADUATE POST

DOCTORATE GRADUATE

C N % C N % C N % C N % C N % C N %

AXIS 4 2% 10 6% 14 8% 89 51% 57 33% 1 1%

HDFC 2 1% 8 5% 0 0% 79 45% 86 49% 0 0%

ICICI 1 1% 12 7% 6 3% 79 45% 75 43% 2 1%

CITI 0 0% 6 6% 8 8% 51 51% 35 35% 0 0%

HSBC 8 11% 13 17% 3 4% 33 44% 16 21% 2 3%

BOB 6 3% 9 5% 15 9% 89 51% 55 31% 1 1%

SBI 2 1% 7 4% 6 3% 62 35% 94 54% 4 2%

Total 23 2% 65 6% 52 5% 482 46% 418 40% 10 1%

Chart 3.1(D): Customer’s Education Qualification and Bank Brand

3.2.1.5: Customers’ Occupation and Bank Brand

The customer‟s were asked to give the data regarding their occupations, and

the output which the researcher received is as under:

Table 3.1(E): Customer’s Occupation and Bank Brand

Bank/

Occupati

on

STUDENT SERVICE BUSINESS PROFESSION

AL RETIRED

HOUSE-

WIFE

C N % C N % C N % C N % C N % C N

%

AXIS 17 10% 106 61% 25 14% 25 14% 0 0% 2 1%

HDFC 8 5% 120 69% 22 13% 21 12% 3 2% 1 1%

ICICI 12 7% 115 66% 12 7% 34 19% 0 0% 2 1%

CITI 6 6% 59 59% 35 35% 0 0% 0 0% 0 0%




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HSBC 3 4% 28 37% 28 37% 8 11% 3 4% 5 7%

BOB 48 27% 84 48% 22 13% 19 11% 0 0% 2 1%

SBI 35 20% 88 50% 19 11% 25 14% 5 3% 3 2%

Total 129 12% 600 57% 163 16% 132 13% 11 1% 15 1%

Chart 3.1(E): Customer’s Occupation and Bank Brand

Looking at Table 3.1(E), we can see that 57 percent of the respondents were

doing Service, 16 percent were associated with Business and 13 percent were

professionals such as Chartered Accountants, Doctors, Lawyers, etc. If we look

further we can find that majority of the CITI Bank‟s customer are associated

with business, so we can say that CITI bank is more associated with Business-

Class People.

3.2.1.6: Customers’ Total Experience with Bank

Looking at Table 3.1(F), we can find that 49 percent of the customers are

banking with their current bank since 2-5 Years only. Whereas we can see that

in the segment of 21-30 Years no customer has given any response. Well we

can find that in the segment of 11-20 Years, Bank of Baroda (21%) is leading

followed by SBI (14%) because they are old public sector banks and operating

since more than 25 years in major cities of Gujarat. One more thing to notice

here is that in the segment of less than 2 Years Axis Bank (33%) is leading




e194

followed by HDFC Bank (30%), which are the new generation and fast

growing banks.

Table 3.1(F): Customer’s Total Experience with Bank Brand

Brand/Experience Less than 2

Yrs.

2-5

Yrs

6-10

Yrs.

11-20

Yrs.

21-30

Yrs

More than 30

Yrs.

AXIS 33% 50% 11% 6% 0% 0%

HDFC 30% 57% 8% 5% 0% 0%

ICICI 23% 51% 15% 11% 0% 0%

CITI 21% 38% 34% 7% 0% 0%

HSBC 17% 60% 20% 3% 0% 0%

BOB 22% 38% 14% 21% 0% 5%

SBI 19% 50% 14% 14% 0% 3%

Total 25% 49% 14% 10% 0% 2%

Chart 3.1(F): Customer’s Total Experience with Bank Brand

3.2.1.7: Customers’ Avg. Annual Balance with Bank Brand

Looking at Table 3.1(G), we can see that 49 percent of the respondents are

keeping their annual balance near about 1 to 3 lac and 25 percent having less

than 1 lac with their respective bank. In the segment of 4 to 5 lac. Citi Bank is

leading followed by HSBC Bank. Whereas we can find that almost all the

banks are having diversified accounts with different bank balances.




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Table 3.1(G): Customers’ Average Annual Balance in his Bank A/C and Bank Brand

Bank

Name

Customer's Average Annual Balance

Less Than 1

Lac.

1 Lac. to 3

Lac.

4 Lac. to 5

Lac.

6 Lac. to 10

Lac.

11 Lac. to

20 Lac.

C N % C N % C N % C N % C N %

AXIS 58 33% 88 50% 19 11% 7 4% 3 2%

HDFC 55 31% 101 58% 10 6% 6 3% 3 2%

ICICI 41 23% 89 51% 20 11% 20 11% 5 3%

CITI 21 21% 38 38% 31 31% 10 10% 0 0%

HSBC 13 17% 45 60% 15 20% 2 3% 0 0%

BOB 39 22% 66 38% 24 14% 37 21% 9 5%

SBI 34 19% 87 50% 24 14% 24 14% 6 3%

Total 261 25% 514 49% 143 14% 106 10% 26 2%

Chart 3.1(G): Customer's Average Annual Balance in his Bank A/C and Bank Name

3.2.1.8: Customers’ proportion of total financial needs he

banks with his Bank Brand.

Looking at Table 3.1(H), we can see that most of the customers don‟t depend

on a single bank for their all type of banking transactions as only 11 percent of

the customers responded that they bank 81% -100% with their bank for various

type of financial needs i.e. saving accounts, current accounts, home loans, auto

loans, mutual funds, insurance, etc. Whereas, here we can find that majority of




e196

the customers 35% and 26% responded that they do only 21%-40% and 0%-

20% of their financial business with the banks respectively.

Table 3.1(H): Customer’s proportion of total financial needs bank with his

Bank Brand.

Bank

Name

What proportion of customer‟s average annual total business he/she bank

with this bank?

0%-20% 21%-40% 41%-60% 61%-80% 81%-100%

C N % C N % C N % C N % C N %

AXIS 41 23% 39 22% 40 23% 19 11% 36 21%

HDFC 37 21% 56 32% 43 25% 20 11% 19 11%

ICICI 48 27% 46 26% 43 25% 16 9% 22 13%

CITI 28 28% 72 72% 0 0% 0 0% 0 0%

HSBC 16 21% 35 47% 11 15% 6 8% 7 9%

BOB 56 32% 62 35% 30 17% 15 9% 12 7%

SBI 44 25% 54 31% 33 19% 24 14% 20 11%

Total 270 26% 364 35% 200 19% 100 10% 116 11%

Chart 3.1(H): Customers’ proportion of total financial needs he banks with his Bank Brand.

3.2.2: Bank Managers’ Profile

The researcher has surveyed total 35 branch managers from pre-specified seven

banks of six major cities of Gujarat. Most of the respondents were Branch




e197

Managers and Senior Level Managers in their respective bank and the

demographic characteristics of the managers surveyed under the study were as

following:

3.2.2.1 Managers’ Bank and City

Looking at Table 3.1(I), we can find that the researcher has taken a sample of

at least one branch manager from each bank and each city. However the CITI

Bank and HSBC banks are not operating their branches is some of the cities

under our research area.

Table 3.1(I): Manager’s Bank Brand and City

City/Bank Name AXIS HDFC ICICI CITI HSBC BOB SBI Total


AHMEDABAD 1 1 1 1 1 1 1 7

VADODARA 1 1 1 1 1 1 1 7

SURAT 1 1 1 1 0 1 1 6

RAJKOT 1 1 1 0 0 1 1 5

BHAVNAGAR 1 1 1 0 0 1 1 5

JAMNAGAR 1 1 1 0 0 1 1 5

Total 6 6 6 3 2 6 6 35

Chart 3.1(I): Manager’s Bank Brand Name and City

3.2.2.2 Managers’ Age and Bank

Looking at Table 3.1(J), we can find that SBI is having managers with 100

percent in the age group of 41-50, where as we can find the private banks are

having younger managers in the age group of 21-30 and 31-40.




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Table 3.1(J): Manager’s Age and Bank Name

Bank

Name

/ Age

10-20 21-30 31-40 41-50 MORE THAN

50 Total

C N

% C N % C N % C N % C N % C N %

AXIS 0 0% 5 83% 0 0% 1 17% 0 0% 6 100%

HDFC 0 0% 5 83% 1 17% 0 0% 0 0% 6 100%

ICICI 0 0% 5 83% 1 17% 0 0% 0 0% 6 100%

CITI 0 0% 3 100% 0 0% 0 0% 0 0% 3 100%

HSBC 0 0% 1 50% 1 50% 0 0% 0 0% 2 100%

BOB 0 0% 3 50% 0 0% 0 0% 3 50% 6 100%

SBI 0 0% 0 0% 0 0% 6 100% 0 0% 6 100%

Total 0 0% 22 63% 3 9% 7 20% 3 9% 35 100%

Chart 3.1(J): Manager’s Age and Bank Brand

3.2.2.3 Managers’ Education Qualification and Bank Brand

Looking at Table 3.1(K), we can find that the SBI is having all the managers

with graduate degrees and the private sector banks are having most of its

managers with post graduate degrees.

Table 3.1(K): Manager’s Education Qualification and Bank Brand

Bank Name GRADUATE POST GRADUATE DIPLOMA Total

C N % C N % C N % C N %

AXIS 1 17% 5 83% 0 0% 6 100%

HDFC 0 0% 4 67% 2 33% 6 100%

ICICI 1 17% 5 83% 0 0% 6 100%




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CITI 0 0% 3 100% 0 0% 3 100%

HSBC 1 50% 1 50% 0 0% 2 100%

BOB 2 33% 4 67% 0 0% 6 100%

SBI 6 100% 0 0% 0 0% 6 100%

Total 11 31% 22 63% 2 6% 35 100%

Chart 3.1(K): Manager’s Education Qualification and Bank Brand

3.2.2.4: Managers’ Experience and Bank Brand

When the researcher asked about the Manger‟s Experience with the same bank he

received following responses:

Table 3.1(L): Manager’s Experience and Bank Name

Bank Name/

Experience

Total Experience with this Bank:

0-5 6-10 11-15 16-20 MORE

THAN 20

C N % C N % C N % C N % C N %

AXIS 6 100% 0 0% 0 0% 0 0% 0 0%

HDFC 3 50% 3 50% 0 0% 0 0% 0 0%

ICICI 5 83% 1 17% 0 0% 0 0% 0 0%

CITI 1 33% 2 67% 0 0% 0 0% 0 0%

HSBC 2 100% 0 0% 0 0% 0 0% 0 0%

BOB 0 0% 0 0% 1 17% 2 33% 3 50%

SBI 0 0% 0 0% 0 0% 2 33% 4 67%

Total 18 51% 5 14% 0 0% 4 11% 8 23%

Source: Primary data collected for the study




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Looking at Table 3.1(L), we can find that the SBI is having 67 percent of

managers with experience of more than 20 years with SBI, where as the

foreign bank like CITI and HSBC are having their managers with experience

of 0-5 year only with their bank.

Chart 3.1(L): Manager’s Experience and Bank Name

3.2.3: Analysis of Research Objectives

The researcher has tried to find the answers for the pre-specified research

objectives. Let us explore the finding of all research objectives one by one in the

context of current research.

3.2.3.1: RO_01: To explore the role of brand in retaining existing

customers and attracting potential customers in banks:

Here (Table 3.2(A)) the researcher has found the main reason behind banking

with their existing bank brand is „Past Experience‟ and „Quality of Service‟.

Whereas, we can find that „Price‟ and „Well Advertised‟ are not the influencing

factors for customers.




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One more attempt was made to find out the meaning of bank‟s brand logo as per the

different manager‟s view from the pre-specified banks. Here (Table 3.2 (B)), we can

see that for Axis bank, most of the manager‟s responded that it is a „Sign of Stability

& Support‟, for HDFC it stands for „Bank Understands the needs and requirements of

all the customers & Employees‟, for ICICI it stands for „'I' stands for ICICI bank, for

Citi bank its „Umbrella : All solutions under one roof‟, for HSBC bank its

„Hexagon: Want to touch all the corners of the World‟, for Bank Baroda its

Raising Sun: The Bank is raising Day by Day‟, for State Bank its „Complete

Development in all direction with Customer at the center of Circle‟.

Table 3.2(B): Meaning of the Brand Logo of various banks as per managers view.

Bank Name

What meaning you bank-logo

convey?

AXIS HDFC ICICI CITI HSBC BOB SBI

Count Count Count Count Count Count Count

No Response( Blanks) 1 5 3 0 0 0 2

AXIS :

It‟s a base of entire economy

actually moving.

1 0 0 0 0 0 0

Sign of Stability & Support 1 0 0 0 0 0 0

Upward stability 1 0 0 0 0 0 0

We have only change the name

nothing else

1 0 0 0 0 0 0

All type of financial solutions

for customers under one roof

1 0 0 0 0 0 0

HDFC:

Bank Understands the needs and

requirements of all the

customers & Employees

0 1 0 0 0 0 0

ICICI:

'I' stands for ICICI bank 0 0 2 0 0 0 0

Table 3.2(A): Important Reasons behind banking with existing bank brand- Customers Reasons Frequency N% Rank

Past Experience 508 29% 1

Quality 400 23% 2

Personal Recommendations 324 19% 3

Price 208 12% 4

Well Known/Advertised 185 11% 5

Ratings in consumer Report 122 7% 6

Total 1747 100%




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We are always there with you 0 0 1 0 0 0 0

CITI:

Trust & Stability 0 0 0 2 0 0 0

Umbrella : All solutions under

one roof

0 0 0 1 0 0 0

HSBC:

Hexagon: Want to touch all the

corners of the World

0 0 0 0 1 0 0

World's Local Bank 0 0 0 0 1 0 0

BANKBARODA:

Raising Sun: The Bank is raising

Day by Day

0 0 0 0 0 6 0

SBI:

Solutions for everybody under

one roof

0 0 0 0 0 0 2

Accurate Customer Service 0 0 0 0 0 0 1

Complete Development in all

direction with Customer at the

center of Circle

0 0 0 0 0 0 1

TOTAL: 6 6 6 3 2 6 6


In order to find out what strength a bank brand is having in retaining existing

customers as well as acquiring the new customers, the mangers replied to these

questions effectively and the responses are as below (Table 3.2(C)). ICICI

mangers do consider 8 to 8 branch timing as their most unique feature.

Table 3.2(C): What is most unique about your Bank Brand- (Mangers)

What is most unique about your bank?

Bank Name



1) No Response (Blank) 2 2 2 0 0 3 3

Banking for everyone 0 0 0 0 0 2 0

Best Customer Service 0 0 0 2 0 0 0

Branch and ATM access in Any City 0 0 1 0 0 0 0

Branch Location 0 0 0 0 1 0 0

Brand 1 0 0 0 0 0 0

Customer Service 1 0 0 0 0 0 0

Customer Service and Relationship 1 0 0 0 0 0 0

Focus on Quality of Customer Services 0 0 0 1 0 0 0

High quality Customer services 0 1 0 0 0 0 0

Micro Lending loans 0 0 1 0 0 0 0

Networking of all branches under core-banking 0 0 0 0 0 0 2

Never posted any Loss since 102 Years 0 0 0 0 0 1 0

No of Branches 0 0 0 0 0 0 1

pioneer in every front of banking business 0 0 1 0 0 0 0

Relationship Banking 0 1 0 0 0 0 0

Uniformity For All 0 0 0 0 1 0 0

Unique Logo 1 0 0 0 0 0 0




e203

Universal Banking 0 0 1 0 0 0 0

Word of Mouth Publicity 0 2 0 0 0 0 0


8 To 8 Branch Timings 0 0 1 0 0 0 0

Best service to Key Customers 0 2 0 0 0 0 0

Increase in Customer Base 0 0 0 0 0 0 1

Innovative Ideas 0 0 0 2 0 0 0

Latest brand building activities 0 0 1 0 0 0 0

Slow & steady growth 1 0 0 0 0 0 0


40% Faster ATMs 0 0 1 0 0 0 0


3.2.3.2: RO_02: To know the top of the mind bank brands among

customers and managers.

The Researcher tried to find the Top of Mind brands among customers by asking

open ended question like “Fill in the below given blanks by the name of banks

you know very well” (Q.16, Customer‟s Questionnaire).

The research found that „State Bank of India‟ scores the highest (refer Table

3.3(A)) followed by ICICI Bank, Bank of Baroda, HDFC Bank and AXIS Bank.

Whereas we can find that in the foreign sector banks HSBC (6th Rank), CITI (7th

Rank), ABN AMRO (16th) (Now known as RBS: Royal Bank of Scotland),

Standard Charter Bank (19th) scores high on the Top of the mind bank brands

list.

Table 3.3(A): Top of the mind Bank Brands (Overall)

Bank Brand Total Score Rank

STATE BANK OF INDIA (SBI) 783 1

ICICI BANK 747 2

BANK OF BARODA (BOB) 627 3

HDFC BANK 600 4

AXIS BANK 471 5

HSBC BANK 296 6

CITI BANK 182 7

DENA BANK 128 8

BANK OF INDIA (BOI) 127 9

PUNJAB NATIONAL BANK (PNB) 110 10

IDBI BANK 100 11




e204

OTHER LOCAL Co-operative BANKS 69 12

KOTAK BANK 47 13

CENTRAL BANK OF INDIA 38 14

UNION BANK OF INDIA 35 15

ABN AMRO BANK 27 16

CORPORATION BANK 26 17

CANARA BANK 24 18

STANDARD CHARTERED BANK 22 19

INDIAN OVERSEAS BANK 20 20

ORIENTAL BANK OF COMMERCE (OBC) 15 21

UCO BANK 13 22

INDIAN BANK 12 23

SYNDICATE BANK 11 24

YES BANK 9 25

DEVELOPMENT AND CREDIT BANK (DCB) 4 26

INDUS IND BANK 2 27 Source: Primary data collected for the study

Table 3.3(B): Top of the mind Bank Brands (Foreign Sector)


HSBC BANK 296 1

CITI BANK 182 2

ABN AMRO BANK 27 3

STANDARD CHARTERED BANK 22 4


Table 3.3(D): Top of the mind Bank Brands (Public Sector)


STATE BANK OF INDIA (SBI) 783 1

BANK OF BARODA (BOB) 627 2

DENA BANK 128 3

BANK OF INDIA (BOI) 127 4

PUNJAB NATIONAL BANK (PNB) 110 5

IDBI BANK 100 6

CENTRAL BANK OF INDIA 38 7

Table 3.3(C): Top of the mind Bank Brands (Private Sector)


ICICI BANK 747 1

HDFC BANK 600 2

AXIS BANK 471 3

KOTAK BANK 47 4

YES BANK 9 5

DEVELOPMENT AND CREDIT BANK (DCB) 4 6




e205

UNION BANK OF INDIA 35 8

CORPORATION BANK 26 9

CANARA BANK 24 10

INDIAN OVERSEAS BANK 20 11

ORIENTAL BANK OF COMMERCE (OBC) 15 12

UCO BANK 13 13

INDIAN BANK 12 14

YNDICATE BANK 11 15

INDUS IND BANK 2 16

When the

researcher asked to

identify the logo of

the following

banks, the Bank of

Baroda came out as

the no.1 in terms of

brand identification.

The Rising Sun

denoting „B‟ has

better brand

awareness then the

SBI followed by

AXIS and ICICI

stood 2nd

and 3rd

.

When the researcher

asked customers, to

indentify the corporate

colors of their bank

brand, again the Bank of

Baroda stood at first

because of its shining

orange color which is

easily identifiable by the

customers. SBI and

ICICI bank stood 2nd

and 3rd on the table.

Table 3.3(E): Indentify the following of logos of a bank brand (q.17)

Bank Brand Logo Total Score Rank

BANK of BARODA (BOB)

956 1

STATE BANK OF INDIA (SBI)

941 2

AXIS BANK

922 3

CICI BANK

910 4

CITI BANK 891 5

HDFC BANK

780 6

HSBC BANK

683 7

Table 3.3(F): Identify the corporate color of your bank brand?

( Q19)

Bank Brand Logo (%) Rank

BOB

64.57 1

SBI

62.29 2

ICICI

57.14 3

HSBC

50.67 4

AXIS

49.71 5

CITI

45.00 6

HDFC 40.00 7




e206

Table 3.3(G): Match the Brand Slogan (Punch line) with Respective bank

Bank Brand Punch Line Total Score Rank

ICICI Hum Hain Na 586 1

SBI Pure Banking Nothing Else 418 2

HSBC World's Local Bank 393 3

HDFC We Understand your World 361 4

AXIS Every Thing is the Same Except the Name 331 5

BOB India's International Bank 290 6

CITI Let's Get it Done 248 7

Looking at Table 3.3(G), we can find that the punchline of ICICI bank is highly

known by customers in the market, followed by SBI and HSBC.

Chart 3.1(M): Rank of

use of different Bank

Accounts.

Looking at Chart

3.1(M) and Table

3.3(H), we can find

that for the first rank

customer prefer SBI

followed by HDFC,

ICICI and BOB. If we

look further we can

conclude the foreign

banks are not able to

score good on first

rank as people might

be operating their

secondary accounts

with such bank, where

as for the primary

account SBI, HDFC

and ICICI followed by

BOB, scores highest in

all banks.




e207

Table 3.3(H): Rank of Use of Bank Accounts

Bank Name Rank of Use

R1 R2 R3 R4 R5 R6

SBI 223 189 81 34 20 1

HDFC 188 89 52 33 39 4

ICICI 185 135 82 41 21 5

BOB 155 90 62 46 31 10

AXIS 136 80 60 46 53 9

CITI 73 25 21 41 28 14

HSBC 40 34 26 31 24 9

UCO 12 15 15 4 3 0

OTHERS (Local Cooperatives) 12 15 15 4 3 0

BANK OF INDIA 4 1 0 2 1 0

PNB 4 0 4 0 0 0

UBI 3 4 2 0 0 0

IDBI 3 0 0 0 0 0

KOTAK 2 3 0 1 1 0

STANDARD CHARTERED 2 3 0 0 0 0

DENA 2 1 0 0 0 0

INDIAN OVERSEAS 2 0 0 0 0 0

CORPORATION BANK 2 0 0 0 0 0

ABNAMRO 1 0 1 0 0 0

INDUSIND 0 2 0 0 0 0

ORIENTAL BANK OF COMMERCE 0 1 0 0 0 0

CENTRAL BANK OF INDIA 0 0 1 0 0 0

SYNDICATE BANK 0 0 1 0 0 0

Table 3.3(I): Bank Logo Identification By Managers

Logo

Managers of Banks


Sum Sum Sum Sum Sum Sum Sum Sum

HSBC 5 6 5 3 2 4 5 30

SBI 6 6 6 3 2 6 6 35

CITI 6 6 6 3 2 6 6 35

HDFC 6 6 5 3 2 4 5 31

ICICI 6 6 6 3 2 5 5 33

BOB 6 6 6 3 2 6 6 35

AXIS 6 6 6 3 2 4 5 32




e208

Table 3.3(J): Bank Brand Punchline Identification By Managers

PUNCH LINE Managers of Banks


Sum Sum Sum Sum Sum Sum Sum Sum

HDFC 5 4 5 2 0 0 0 16

ICICI 5 6 6 3 1 2 1 24

AXIS 5 1 6 0 1 4 2 19

SBI 4 4 5 0 1 3 4 21

BOB 3 2 5 0 1 3 0 14

CITI 3 3 5 3 0 3 0 17

HSBC 4 4 5 3 1 0 3 20


Looking at the above Table 3.3(I),we can see that the brand logos of SBI, CITI

and BOB are identified by all the managers, again Table 3.3(J), we can see that

ICICI bank scores high in terms of Punchline identification, followed by SBI,

which is same as per the results derived from the customers (Table 3.3(G)).

In short we can say that ICICI and SBI bank are scoring high as „Top of Mind

bank Brands‟ in the Gujarat Region.

3.2.3.3: RO_03: To study the use of different brand elements in

managing brand equity in banks.

The researcher has tried to find the effect of various brand elements on brand by

using the readily available models like Interbrand‟s Brand Valuation Model,

SERVQAL model for service quality, and Determinants of Corporate Image

Model (for finding out the key elements which are important in each corporate

brand).

Let us use all these modes one by one.

1. Interbrand’s Brand Valuation Model




e209

2. The researcher has asked bank managers to rate their respective bank brand on

the following parameters (Using Interbrand Brand Valuation Model, Refer to

chapter 1.1.45) and below given table show the result for each bank:

Table 3.4(A): Interbrand’s Brand Valuation (Means for Banks)

Factors

Bank Name


Mean Mean Mean Mean Mean Mean Mean

Leadership 5.00 5.00 4.67 5.00 4.00 3.00 4.17

Stability 4.67 4.83 4.67 4.33 4.50 4.50 4.50

Market 4.17 3.83 4.83 4.33 4.50 4.00 4.33

Internationality 2.67 2.50 4.67 5.00 4.00 4.17 3.50

Trend 3.83 3.00 4.33 4.33 4.00 3.33 3.67

Support 3.83 3.83 4.33 5.00 4.00 4.33 3.17

Brand Protection 4.17 4.17 4.50 5.00 4.00 3.67 3.17


Table 3.4(B) Interbrand's Brand Valuation Model

Factors Weight

(%)

Bank Name


Leadership 25 125 125 117 125 100 75 104

Stability 15 70 73 70 65 68 68 68

Market 10 42 38 48 43 45 40 43

Internationality 25 67 63 117 125 100 104 88

Trend 10 38 30 43 43 40 33 37

Support 10 38 38 43 50 40 43 32

Brand Protection 5 21 21 23 25 20 18 16

Total 100 401 388 461 477 413 382 387

Rank 4 5 2 1 3 7 6





e210

A. Reliability of Scale (Cronbach’s Alpha):

Table3.4(C) Reliability Statistics (Interbrand’s Brand

Valuation)

Cronbach's

Alpha

Cronbach's Alpha Based on

Standardized Items

N of Items

0.721 0.707 7

Table 3.4(D): Item-Total Statistics (Interbrand’s Brand Valuation)

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if

Item Deleted

Leadership 24.26 15.314 0.178 0.4 0.746

Stability 24.06 16.879 0.057 0.242 0.75

Market 24.4 13.424 0.541 0.443 0.667

Internationality 25 12.588 0.375 0.542 0.713

Trend 24.94 12.055 0.656 0.666 0.631

Support 24.66 11.997 0.654 0.784 0.631

Brand

Protection

24.63 12.064 0.58 0.762 0.648

B. Factor Analysis:

Table 3.4(E): KMO and Bartlett's Test (Interbrand’s Brand

Valuation)

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy.

.480

Bartlett's Test of

Sphericity

Approx. Chi-Square 93.719

Df 21

Sig. .000

Table 3.4(F): Communalities (Interbrand’s Brand Valuation)

Initial Extraction

Leadership 1.000 .906

Stability 1.000 .595




e211

Market 1.000 .517

Internationality 1.000 .835

Trend 1.000 .808

Support 1.000 .840

Brand Protection 1.000 .708

Extraction Method: Principal Component Analysis.

Table 3.4(G): Component Matrix (Interbrand’s Brand Valuation)

Component

1 2 3

Leadership 0.312 -0.227 0.870

Stability 0.131 0.757 0.065

Market 0.712 0.022 0.101

Internationality 0.611 -0.414 -0.539

Trend 0.775 -0.449 0.076

Support 0.807 0.382 -0.209

Brand Protection 0.769 0.323 0.113


Total Variance Explained: 74.42%.

Table 3.4(H): Rotated Component Matrix

(Interbrand’s

Brand Valuation)

Component

1 2 3

Leadership 0.066 -0.009 0.950

Stability 0.582 -0.491 -0.123

Market 0.558 0.352 0.286

Internationality 0.225 0.863 -0.201

Trend 0.307 0.735 0.417

Support 0.872 0.274 -0.075

Brand Protection 0.794 0.165 0.226




e212

C. Attribute Based Perceptual Mapping (Interbrand’s Brand

Valuation)

Table 3.4(I): Group Statistics (Interbrand’s Brand Valuation)

Bank Name Mean Std. Deviation Valid N (listwise)

Unweighted Weighted

AXIS Leadership 5 0 6 6

Stability 4.67 0.516 6 6

Market 4.17 0.983 6 6

Internationality 2.67 1.506 6 6

Trend 3.83 1.169 6 6

Support 3.83 1.169 6 6

Brand Protection 4.17 0.983 6 6

HDFC Leadership 5 0 6 6

Stability 4.83 0.408 6 6

Market 3.83 1.472 6 6


Trend 3 0.894 6 6

Support 3.83 0.753 6 6


ICICI Leadership 4.67 0.816 6 6

Stability 4.67 0.516 6 6

Market 4.83 0.408 6 6


Trend 4.33 1.211 6 6

Support 4.33 1.033 6 6


CITI Leadership 5 0 3 3

Stability 4.33 1.155 3 3

Market 4.33 1.155 3 3

Internationality 5 0 3 3

Trend 4.33 1.155 3 3

Support 5 0 3 3

Brand Protection 5 0 3 3

HSBC Leadership 4 0 2 2

Stability 4.5 0.707 2 2

Market 4.5 0.707 2 2

Internationality 4 0 2 2

Trend 4 0 2 2

Support 4 0 2 2

Brand Protection 4 0 2 2

BOB Leadership 3 1.095 6 6

Stability 4.5 0.548 6 6

Market 4 0 6 6





e213

Trend 3.33 0.516 6 6

Support 4.33 0.816 6 6


SBI Leadership 4.17 0.753 6 6

Stability 4.5 0.837 6 6

Market 4.33 0.516 6 6


Trend 3.67 0.816 6 6

Support 3.17 1.169 6 6


Total Leadership 4.4 0.946 35 35

Stability 4.6 0.604 35 35

Market 4.26 0.852 35 35


Trend 3.71 0.987 35 35

Support 4 1 35 35

Brand Protection 4.03 1.071 35 35 Source: Primary data collected for the study

Table 3.4(J): Eigenvalues (Interbrand’s Brand Valuation)

Function Eigenvalue % of Variance Cumulative % Canonical Correlation

1 3.468a 70.8 70.8 0.881

2 .801a 16.4 87.1 0.667

3 .460a 9.4 96.5 0.561

4 .125a 2.5 99.1 0.333

5 .043a 0.9 100 0.203

6 .002a 0 100 0.045

a. First 6 canonical discriminant functions were used in the analysis.

Table 3.4(K): Wilks' Lambda (Interbrand’s Brand Valuation)

Test of Function(s) Wilks' Lambda Chi-square df Sig.

1 through 6 0.072 70.893 42 0.003

2 through 6 0.323 30.473 30 0.442

3 through 6 0.583 14.581 20 0.8

4 through 6 0.851 4.362 12 0.976

5 through 6 0.957 1.191 6 0.977

6 0.998 0.054 2 0.973




e214

Table 3.4(L): Structure Matrix (Interbrand’s Brand Valuation)

Function

1 2 3 4 5 6

Leadership .608* 0.481 0.196 0.179 -0.439 0.311

Internationality -0.372 .813* 0.215 0.118 -0.078 0.211

Support -0.084 .493* -0.47 0.403 0.48 0.244

Market -0.037 0.284 .451* 0.17 0.449 0.123

Trend -0.025 0.436 0.419 .717* -0.003 0.345

Stability 0.092 -0.062 -0.041 -0.234 .716* 0.62

Brand Protection 0.113 0.519 -0.283 0.377 .560* -0.314

Pooled within-groups correlations between discriminating variables and

standardized canonical discriminant functions

Variables ordered by absolute size of correlation within function.

*. Largest absolute correlation between each variable and any discriminant function

Table 3.4(M): Standardized Canonical Discriminant Function Coefficients

(Interbrand’s Brand Valuation)

Function

1 2 3 4 5 6

Leadership 0.922 0.458 -0.09 -0.326 -0.383 0.281

Stability 0.562 -0.011 0.236 -0.305 0.748 0.384

Market -0.421 0.08 0.578 -0.433 0.125 0.109

Internationality -0.527 0.930 0.193 -0.671 -0.083 -0.066

Trend 0.088 -0.513 0.495 1.351 0.161 0.122

Support -0.814 -0.053 -1.274 0.28 -0.52 1.008

Brand Protection 0.968 0.482 0.286 0.154 0.865 -1.232

Table 3.4(N) Functions at Group Centroids (Interbrand’s Brand Valuation)

Bank Name Function

1 2 3 4 5 6

AXIS 1.763 -0.53 0.009 0.54 0.029 0.023

HDFC 2.083 -0.274 -0.618 -0.437 0.048 -0.015

ICICI -0.179 1.073 0.578 -0.102 0.239 0.024

CITI -0.041 1.78 -0.612 0.182 -0.385 -0.011

HSBC -0.951 -0.093 0.389 0.288 0.146 -0.152

BOB -2.861 -0.491 -0.662 0.022 0.063 0.018

SBI -0.469 -0.637 0.869 -0.21 -0.235 0.006

Unstandardized canonical discriminant functions evaluated at group means




e215

Table 3.4(O): Classification Resultsa

(Interbrand’s Brand Valuation)

Bank Name

Predicted Group Membership Total


Count

AXIS 3 1 2 0 0 0 0 6

HDFC 1 4 0 1 0 0 0 6

ICICI 0 0 5 0 1 0 0 6

CITI 0 0 2 1 0 0 0 3

HSBC 0 0 0 0 2 0 0 2

BOB 0 0 1 0 0 5 0 6

SBI 1 0 1 0 2 0 2 6

%

AXIS 50 16.7 33.3 0 0 0 0 100

HDFC 16.7 66.7 0 16.7 0 0 0 100

ICICI 0 0 83.3 0 16.7 0 0 100

CITI 0 0 66.7 33.3 0 0 0 100

HSBC 0 0 0 0 100 0 0 100

BOB 0 0 16.7 0 0 83.3 0 100

SBI 16.7 0 16.7 0 33.3 0 33.3 100

a. 62.9% of original grouped cases correctly classified.

Chart 3.2(A): Canonical Discriminant Function (Interbrand’s Brand Valuation)




e216

Chart 3.2(B): Attribute Based Perceptual Mapping of Banks (Interbrand’s Brand Valuation)

Analysis & Interpretation:

1. Looking at Table 3.4(B), We can see that CITI Bank secures the 1st Rank

on the Interbrand‟s Brand Valuation Model followed by ICICI Bank and

HSBC Bank in India, where as Indian Public sector banks scores low

because of lack of Trend, and Internationality, Please not that these Ranks

computed from the inputs and rankings given by the branch managers of

the above banks.

2. Looking at Table 3.4(C), Cronbach‟s Alpha value is 0.721, which indicates

higher reliability of the scale. Further we can see in Table 3.4(D), Column

6, we can find that almost all the scale score Alpha Value of greater than

0.5, which again indicates a better reliability of the scale, and now we can

proceed with all the variables for further analysis.

3. Looking at Table 3.4(G) (Component Matrix), we can find three different

components out of which Component 1 is made of Market,




e217

Internationality, Trend, Support, Brand Protection, Component 2 is made

of Stability and Component 3 consist of Leadership.

4. Looking at Table 3.4(H) (Rotated Component Matrix), we can find three

different components out of which component 1 is made of Stability,

Market, Support and Brand Protection, Component 2 is made of

Internationality and Trend where as Component 3 consist of Leadership.

5. Looking at table 3.4(M), we can see that Dimension 1 is made up of

Leadership, Stability and Brand Protection, where as Dimension 2 is made

up of „Internationality‟.

6. Looking at Chart 3.2(B), Axis Bank and HDFC bank score high on

Dimension-1 (Leadership, Stability and Brand Protection) whereas CITI

and ICICI Bank score high on Dimension -2 (Internationality).

3. Servqual Model (Service Quality Model)

The researcher has asked bank customers to rate their respective bank brand

on the following parameters and below given table shows the result for each

bank:

Table 3.5 (A): Service Quality Parameters

S

r.

N

o.

Statement Regarding Bank

and its Services

SERVQ

UAL

Determi

nants

Bank Name

AXIS HDF

C ICICI CITI

HSB

C BOB SBI


1 Offers fun promotions

Assura

nce

3.45 3.33 3.20 3.68 3.40 3.15 3.09

2 Staff Knowledge

Experience and Expertise 3.86 3.77 3.78 3.85 3.96 3.71 3.45

3

I am proud to have others

know I bank with this

bank

3.56 3.65 3.35 3.13 3.48 3.49 3.65

4 Is understanding/knowing

the customer

Empat

hy

3.50 3.62 3.50 3.46 3.09 3.57 3.50

5 Seating/Waiting

Arrangements at Branch 3.74 3.48 3.70 3.73 4.07 3.71 3.46

6 I really love my bank 3.70 3.67 3.40 3.65 3.56 3.79 3.55

7 I would really miss my

bank if it went away 3.69 3.52 3.21 3.70 3.39 3.45 3.36

8 My bank is more than a

bank to me 3.57 3.23 3.16 3.17 3.55 3.26 3.31

9 I will recommend this 4.05 4.12 3.73 4.14 3.81 4.13 3.99




e218

bank to others

1

0

I really like to talk about

this bank to others 3.50 3.60 3.28 3.38 3.44 3.40 3.51

1

1

I consider myself to be a

loyal customer of this

bank

Reliabi

lity

4.07 3.97 4.00 4.55 4.05 4.22 3.95

1

2

I have never seriously

considered changing my

bank

3.83 3.95 3.61 4.38 3.96 3.97 3.85

1

3

I will use other

products/services offered

by this bank in near future

3.86 3.97 3.73 3.80 3.59 3.73 3.95

1

4

I deal with this bank

because I want to, not

because I have to

3.89 3.92 3.58 4.17 3.76 3.98 3.83

1

5 Has high quality services 3.42 3.73 3.66 4.15 3.69 3.61 3.37

1

6

My bank is easily

reachable 4.19 4.20 4.07 3.40 3.48 4.19 4.21

1

7

Banking Hours/Branch

Timings 3.59 3.70 3.88 3.65 3.32 3.73 3.57

1

8

I can bank with this bank

whenever I want 3.91 3.94 3.89 3.59 3.93 3.66 3.79

2

0

Provides quick, efficient

services

Respon

sivenes

s

3.95 3.94 3.81 4.03 3.68 3.57 3.58

2

1 Has variety of services 3.89 3.93 3.90 3.88 3.89 3.61 3.77

2

2 Staff Response at Branch 3.82 3.85 3.64 4.01 3.55 3.85 3.43

2

3

I am always interested in

learning more about this

bank

3.59 3.74 3.61 3.46 3.16 3.45 3.54

2

4

Compared to other

people, I follow news

about my bank very

closely

3.51 3.61 3.46 3.35 3.56 3.41 3.49

2

5

Has a stylish and

attractive looks

Tangib

ility

3.74 3.60 3.60 4.09 3.59 3.50 3.31

2

6

Physical Ambience

(Cleanliness, Fresh Air,

Air-Conditioning, Etc.)

4.15 4.21 4.05 4.31 4.28 3.82 3.77

2

7 Branch Floor Space 3.98 3.95 3.93 4.00 4.19 3.63 3.82

2

8

Adequate Parking

Facilities at Branch 3.53 3.37 3.18 3.00 2.56 3.39 3.29

2

9

Stationary

Availability(Forms, pins,

Gum,etc.) at Branch

3.82 3.79 3.74 4.23 4.33 3.71 3.56

3

0

Inquiry Counter

(Customer Help Desk) at

Branch

3.97 3.89 3.71 3.94 3.55 3.63 3.45




e219

3

1

Dressing of Staff

Members at Branch 3.55 3.85 3.83 4.13 3.99 3.25 3.19

3

2

I really identify with

people who bank with this

bank

3.51 3.30 3.25 3.15 3.44 3.27 3.41

3

3

I would be interested in

merchandise with my

bank name on it

3.51 3.55 3.34 3.40 3.32 3.44 3.43

TOTAL of Mean Score For Bank 119.

91

119.

95

115.

79

120.

56

116.

61

116.

27

114.

43

Mean for Banks 3.53 3.53 3.41 3.54 3.43 3.43 3.37

Rank for Banks 3 2 6 1 4 5 7


A. Reliability of Scale:

After getting the mean score for each variable, the questions were grouped as

per these defined factors and process further as below.

In order to find the reliability of scale the researcher has used the Conbach‟s

alpha.

Table 3.5(B): Reliability Statistics (SERVQUAL-Customers)

Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items

0.777 0.772 5

Table 3.5(C): Item-Total Statistics (SERVQUAL-Customers)

SERVQUAL-

DETERMINANTS

Scale

Mean

if Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if

Item

Deleted

Tangibility 16.6 7.188 0.64 0.56 0.706

Reliability 16.2 8.459 0.481 0.44 0.76

Responsiveness 16.74 5.667 0.744 0.605 0.658

Assurance 16.57 8.664 0.309 0.351 0.807

Empathy 16.74 6.903 0.619 0.499 0.711




e220

Table 3.5(D): Inter-Item Correlation Matrix (SERVQUAL-Customers)

Tangibility Reliability Responsiveness Assurance Empathy

Tangibility 1 0.626 0.517 0.138 0.608

Reliability 0.626 1 0.429 0.021 0.374

Responsiveness 0.517 0.429 1 0.531 0.602

Assurance 0.138 0.021 0.531 1 0.188

Empathy 0.608 0.374 0.602 0.188 1


B. Factor analysis:

After find the reliability of a scale, the researcher has used factor analysis

techniques on the same scales, to reduce the no of factors which mark the

effectiveness in terms of service quality for the customers.

Table 3.5(E): KMO and Bartlett's Test (SERVQUAL-Customers)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.652

Bartlett's Test of Sphericity Approx. Chi-Square 60.903

df 10

Sig. 0

TOTAL VARIANCE 76.69%

Table 3.5(F): Communalities (SERVQUAL-Customers)

Initial Extraction

Tangibility 1 0.791

Reliability 1 0.702

Responsiveness 1 0.820

Assurance 1 0.884

Empathy 1 0.637


Table 3.5(G): Component Matrixa Communalities (SERVQUAL-Customer)

Component

1 2

Tangibility 0.831 -0.316

Reliability 0.698 -0.464

Responsiveness 0.843 0.332

Assurance 0.426 0.838

Empathy 0.795 -0.064




e221

Table 3.5(H): Rotated Component Matrixa (SERVQUAL-Customers)

Component

1 2

Tangibility 0.880 0.129

Reliability 0.836 -0.064

Responsiveness 0.573 0.701

Assurance -0.037 0.940

Empathy 0.725 0.332


1. Looking at Table 3.5(B): we can find Alpha Value of 0.777, which

indicates a better reliability of a scale, also the Alpha value of more than

0.6 for the entire scale variable which again indicates higher reliability of

a scale items (Malhotra, 2004).

2. With KMO value of 0.652(Table 3.5(E)) we can say that the sample is an

adequate representative of total population

3. Assurance (Table 3.5(E)) with the communality of 0.838, scores the

highest followed by Responsiveness and Tangibility.

4. Looking at Table 3.5(G), we can find that Component 1 consists of

Tangibility, Reliability and Empathy, and Component 2 is made of

Assurance and Responsiveness.

C. Perceptual Mapping

After finding the factors, in order to make the findings more visible the

researcher has used the Attribute based perceptual mapping using multi

dimensional scaling techniques to represent the status of each brand on a

single chart.

Table 3.5(I): Group Statistics (SERVQUAL-Customers)


Unweighted Weighted

AXIS Tangibility 4.5 0.837 6 6

Reliability 4.83 0.408 6 6

Responsiveness 4.83 0.408 6 6

Assurance 4.67 0.516 6 6




e222

Empathy 4.17 0.753 6 6

HDFC Tangibility 4.33 0.516 6 6



Assurance 3.33 1.033 6 6

Empathy 3.33 0.516 6 6

ICICI Tangibility 4.5 0.548 6 6



Assurance 4.17 1.169 6 6

Empathy 4.5 0.837 6 6

CITI Tangibility 5 0 3 3

Reliability 5 0 3 3

Responsiveness 5 0 3 3

Assurance 4.67 0.577 3 3

Empathy 5 0 3 3

HSBC Tangibility 3.5 2.121 2 2

Reliability 4 1.414 2 2


Assurance 3.5 0.707 2 2

Empathy 4 1.414 2 2

BOB Tangibility 3.5 0.548 6 6



Assurance 4.33 0.516 6 6

Empathy 3.5 0.548 6 6

SBI Tangibility 3.5 0.837 6 6



Assurance 4.17 0.408 6 6

Empathy 3.83 1.472 6 6

Total Tangibility 4.11 0.867 35 35

Reliability 4.51 0.702 35 35


Assurance 4.14 0.845 35 35

Empathy 3.97 0.954 35 35





e223

Table 3.5(J): Correlation Matrices (SERVQUAL-Customers)

Tangibility Reliability Responsiveness Assurance Empathy

Tangibility 1 0.673 0.314 0.112 0.607

Reliability 0.673 1 0.433 0.059 0.415

Responsiveness 0.314 0.433 1 0.5 0.492

Assurance 0.112 0.059 0.5 1 0.017

Empathy 0.607 0.415 0.492 0.017 1

Table 3.5(K): Eigenvalues (SERVQUAL-Customers)


1 1.841a 48.6 48.6 0.805

2 1.101a 29 77.6 0.724

3 .667a 17.6 95.2 0.633

4 .144a 3.8 99 0.355

5 .037a 1 100 0.19


Table 3.5(L): Wilks' Lambda (SERVQUAL-Customers)


1 through 5 0.085 69.144 30 0

2 through 5 0.24 39.903 20 0.005

3 through 5 0.505 19.115 12 0.086

4 through 5 0.842 4.801 6 0.57

5 0.964 1.031 2 0.597

Table 3.5(M): Standardized Canonical Discriminant Function Coefficients

(SERVQUAL-Customers)

Function

1 2 3 4 5

Tangibility -1.169 0.833 -0.11 0.304 0.727

Reliability 1.474 0.221 0.098 0.075 -0.036

Responsiveness -0.814 0.773 0.118 -0.089 -1.049

Assurance 0.386 -0.655 0.646 0.697 0.376

Empathy 0.469 -0.865 0.659 -0.849 0.312




e224

Table 3.5(N): Structure Matrix (SERVQUAL-Customer)

Function

1 2 3 4 5

Reliability 0.552 .719* 0.388 -0.071 0.151

Tangibility -0.105 .627* 0.467 -0.111 0.605

Responsiveness -0.119 0.377 .774* -0.031 -0.494

Assurance -0.057 -0.177 .710* 0.676 -0.064

Empathy -0.023 0.102 .703* -0.665 0.229

Table 3.5(O): Unstandardized Canonical Discriminant Function Coefficients

(SERVQUAL-Customers)

Function

1 2 3 4 5

Tangibility -1.554 1.108 -0.146 0.404 0.967

Reliability 2.848 0.427 0.19 0.144 -0.069

Responsiveness -0.829 0.787 0.12 -0.091 -1.068

Assurance 0.498 -0.845 0.833 0.899 0.484

Empathy 0.529 -0.976 0.743 -0.958 0.352

(Constant) -7.332 -2.239 -7.14 -1.874 -2.832

Table 3.5(P): Functions at Group Centroids (SERVQUAL-Customers)

Bank Name Function

1 2 3 4 5

AXIS -0.041 0.609 0.69 0.407 -0.247

HDFC 0.218 1.315 -1.177 0.06 0.076

ICICI -0.787 0.302 0.417 -0.379 0.007

CITI -0.041 0.553 1.288 -0.18 0.341

HSBC -0.424 -0.756 -0.579 -0.885 -0.357

BOB -1.559 -1.317 -0.372 0.286 0.083

SBI 2.331 -0.932 -0.009 0.01 0.028





e225

Table 3.5(Q): Classification Resultsa (SERVQUAL-Customers)

Bank Name



Count

AXIS 4 0 0 2 0 0 0 6

HDFC 1 4 1 0 0 0 0 6

ICICI 0 0 2 2 1 1 0 6

CITI 0 0 1 2 0 0 0 3

HSBC 0 0 0 1 1 0 0 2

BOB 0 0 3 0 0 3 0 6

SBI 0 0 1 0 0 0 5 6

%

AXIS 66.7 0 0 33.3 0 0 0 100

HDFC 16.7 66.7 16.7 0 0 0 0 100

ICICI 0 0 33.3 33.3 16.7 16.7 0 100

CITI 0 0 33.3 66.7 0 0 0 100

HSBC 0 0 0 50 50 0 0 100

BOB 0 0 50 0 0 50 0 100

SBI 0 0 16.7 0 0 0 83.3 100


Chart 3.3(A): Canonical Discriminant Function (SERVQUAL- Managers)




e226

Chart 3.3(B): Attribute Based Perceptual Mapping (SERVQUAL-Customers)


1. Looking at Table 3.5(M), we can see that Function 1 consist of

Reliability and Empathy and Function 2, Tangibility and Responsiveness.

2. Looking at chart 3.3(B) and Table 3.5(P) we can find that SBI is strong

on Dimension-1(Reliability and Empathy), where as HDFC followed by

Axis and CITI are strong on Dimension-2 (Tangibility and

Responsiveness).

4. Determinants of Corporate Image

We will follow the Determinants of Corporate Image model (Chapter 1,

figure 1.14) to find out various factors that influence the brand management

practices of various banks under our study. Somehow we will not be able to

incorporate all the factors given in the above model in our study and we will

regroup the factors based on our factor extraction process as below:




e227

Exhibit 3.1: Factors of Corporate Image

Image Factors Factor Name/Title

Factor 1 SERVICE & SUPPORT

Factor 2 COMMUNICATIONS

Factor 3 SALES FORCE

Factor 4 PRICE

Factor 5 PRODUCT

Factor 6 COMPANY BUSINESS CONDUCT

A. Factor Analysis:

Table 3.6(A): Items, Loadings and communalities, Mangers (Corporate Image)

Component

Communalitie

s

F1 F2 F3 F4 F5 F6

F1: SERVICE & SUPPORT

Customer and Employee

feedback is taken using a

variety of direct and indirect

measures

.760 0.664 0.435 0.283 0.207 0.007 -

0.085

We broadcast the feedback of

customer to our employees

.695 0.669 0.303 0.367 0.098 0.092 0.057

Our organization History helps

us in getting a good business

in this competitive world

.523 0.374 0.086 0.345 0.151 0.364 0.319

All our employees know & use

the brand management

practices in their day-to-day

activities

.852 0.574 0.417 0.357 0.383 0.007 0.274

We leverage the power of

word-of-mouth by using

referral marketing programs

.827 0.625 -

0.015

0.434 0.353 0.328 -

0.121

We have a differential reward

system that rewards Customers

based upon their profit/revenue

contribution

.690 0.723 0.123 0.243 -

0.049

0.292 0.069

We use the concept of

'Relationship Pricing' in

pricing our different

products/services

.775 0.870 0.015 0.065 0.004 0.115 0.028

We do co-branding/affinity

partnering programs to provide

increased value to our

.837 0.897 0.042 0.103 0.050 0.108 0.083




e228

customers

Consider writing separate

marketing plans or even

building two marketing

organizations for Acquisition

and Retention efforts

.857 0.868 0.077 0.280 0.125 -

0.026

0.055

We use information from

customers to design our

products/services

.809 0.623 0.254 0.551 0.114 0.147 0.138

We strengthen the emotional

bonds with our customers by

wishing them on important

occasions

.843 0.443 0.092 0.631 0.430 0.043 -

0.232

we have effective customer

recovery strategies including

guarantees for service failures

.642 0.531 0.523 0.056 0.215 0.170 -

0.091

F2:COMMUNICATIONS

We do use e-commerce for the

online branding of our bank

.735 0.098 0.542 0.051 0.619 0.202 0.072

We do event marketing on

regular basis

.689 0.151 0.671 0.192 0.346 0.240 -

0.040

Orientation & Training .806 0.022 0.734 0.354 0.222 0.266 0.141

Our employees training

programs are designed to

develop the skills required for

acquiring and deepening

customer relationships

.737 0.194 0.731 0.115 0.207 0.298 0.140

Test on regular basis for

checking the knowledge about

organization and its products

.730 -

0.007

0.792 0.207 -

0.007

-

0.117

-

0.217

We do have our own customer

community and we organize

special events for them

.773 0.419 0.708 0.147 0.037 0.105 0.251

We take customer feedback

seriously and reply to them

.835 0.525 0.567 0.098 0.248 0.153 -

0.379

F3: SALES FORCE

Weekly Meetings at the branch

Level

.739 0.476 0.145 0.620 0.057 0.273 -

0.170

Internal Tests, Quizzes &

Contests

.737 0.271 0.344 0.726 0.073 -

0.108

-

0.022

Regular Updates about the

organization

.725 0.223 0.243 0.752 -

0.010

0.223 0.028

Organization History with the

help of movie

.756 -

0.069

0.579 0.575 -

0.083

-

0.220

0.174

Customer-Centric performance

standards are established and

monitored at all customer

touch points

.800 0.430 0.121 0.631 0.197 0.405 -

0.014

Transform product

management in to customer

management

.811 0.189 0.405 0.529 0.410 0.394 0.089




e229

F4: PRICE

Relate branding to customer

equity

.839 0.272 0.309 0.306 0.619 -

0.430

0.093

Monitor the intrinsic reliability

of our customers

.840 0.430 0.490 0.287 0.567 -

0.105

0.021

We do customer segmentation

using Customer Lifetime value

(CLV)/related Metrics

.716 0.241 0.310 0.325 0.653 0.066 0.158

Consider how add on sales and

cross selling can increase

customer equity

.822 -

0.215

0.416 0.006 0.756 0.119 -

0.131

Track customer equity gains &

losses against marketing

programs

.849 0.401 0.131 -

0.214

0.698 -

0.268

-

0.257

Invest in highest value

customers first

.810 0.002 -

0.273

0.082 0.794 0.313 0.038

F5: PRODUCT

Brand Management

responsibility of front-line

employees are clearly defined,

assigned and understood

.764 0.571 0.061 -

0.072

0.185 0.599 -

0.191

Our bank uses technology to

automate marketing, sales &

service functions

.744 0.102 0.278 0.126 -

0.116

0.789 -

0.065

My bank provides customized

services and products to our

key customers

.873 0.276 0.053 0.358 0.342 0.740 0.043

We do cross selling of

products/services to increase

customer share

.779 0.440 0.508 0.135 0.006 0.555 0.015

Look for ways to reduce

acquisition costs

.648 -

0.030

0.045 -

0.069

0.140 0.240 -

0.751

F6: COMPANY BUSINESS CONDUCT

We use the CBBE(Customer

Based Brand Equity) Model

while designing our

products/services

.612 0.082 0.177 -

0.216

0.237 0.337 0.598

B. Attribute Based Perceptual Mapping (Corporate Image):

The determinants of corporate image will be mapped on the chart using the

Attribute Based Perceptual Mapping.

Table 3.6(B): Group Statistics (Corporate Image)

Bank Name Mean Std.

Deviation

Valid N (listwise)

Unweighted Weighted

AXIS SERVICE & SUPPORT 3.444 0.854 6 6

COMMUNICATIONS 3.881 0.736 6 6

SALES FORCE 3.583 0.585 6 6




e230

PRICE 4.056 0.172 6 6

PRODUCT 4.033 0.557 6 6

COMPANY BUSINESS

CONDUCT

4.333 1.033 6 6

HDFC SERVICE & SUPPORT 4.153 0.232 6 6


SALES FORCE 3.583 0.361 6 6

PRICE 3.278 1.163 6 6

PRODUCT 4.533 0.432 6 6

COMPANY BUSINESS

CONDUCT

3.167 0.408 6 6

ICICI SERVICE & SUPPORT 4.111 0.792 6 6


SALES FORCE 3.944 0.981 6 6

PRICE 4.083 0.874 6 6

PRODUCT 4.300 0.713 6 6

COMPANY BUSINESS

CONDUCT

3.500 1.643 6 6

CITI SERVICE & SUPPORT 4.833 0.144 3 3


SALES FORCE 4.889 0.192 3 3

PRICE 4.778 0.096 3 3

PRODUCT 4.800 0.000 3 3

COMPANY BUSINESS

CONDUCT

5.000 0.000 3 3

HSBC SERVICE & SUPPORT 3.625 0.530 2 2


SALES FORCE 3.917 0.354 2 2

PRICE 3.417 0.825 2 2

PRODUCT 3.600 1.697 2 2

COMPANY BUSINESS

CONDUCT

3.000 0.000 2 2

BOB SERVICE & SUPPORT 2.514 0.746 6 6


SALES FORCE 2.222 1.344 6 6

PRICE 3.222 0.344 6 6

PRODUCT 3.200 0.438 6 6

COMPANY BUSINESS

CONDUCT

4.000 0.000 6 6

SBI SERVICE & SUPPORT 2.931 1.141 6 6


SALES FORCE 2.972 0.733 6 6

PRICE 3.361 0.859 6 6

PRODUCT 4.300 0.415 6 6

COMPANY BUSINESS

CONDUCT

3.833 0.983 6 6




e231

Total SERVICE & SUPPORT 3.562 1.007 35 35


SALES FORCE 3.438 1.055 35 35

PRICE 3.691 0.842 35 35

PRODUCT 4.109 0.731 35 35

COMPANY BUSINESS

CONDUCT

3.829 1.014 35 35


Table 3.6(C): Tests of Equality of Group Means (Corporate Image)

Corporate Image - Determinants Wilks' Lambda F df1 df2 Sig.

SERVICE & SUPPORT 0.483 4.992 6 28 0.001

COMMUNICATIONS 0.359 8.333 6 28 0

SALES FORCE 0.505 4.577 6 28 0.002

PRICE 0.651 2.499 6 28 0.046

PRODUCT 0.535 4.059 6 28 0.005

COMPANY BUSINESS CONDUCT 0.701 1.995 6 28 0.1

Table 3.6(D): Pooled within Group Correlation (Determinants of Corporate

Image):Pooled Within-Groups Matrices

Correlation SERVICE &

SUPPORT COMMUNICATIONS

SALES

FORCE PRICE PRODUCT

COMPANY

BUSINESS

CONDUCT

SERVICE & SUPPORT 1 0.601 0.539 0.484 0.395 0.324

COMMUNICATIONS 0.601 1 0.502 0.475 0.421 0.192

SALES FORCE 0.539 0.502 1 0.23 0.259 0.033

PRICE 0.484 0.475 0.23 1 0.202 0.011

PRODUCT 0.395 0.421 0.259 0.202 1 0.311

COMPANY BUSINESS

CONDUCT 0.324 0.192 0.033 0.011 0.311 1

Table 3.6(E): Eigen values (Corporate Image)


1 1.981a 41.9 41.9 0.815

2 1.751a 37.1 79 0.798

3 .658a 13.9 92.9 0.63

4 .280a 5.9 98.8 0.468

5 .044a 0.9 99.8 0.205

6 .011a 0.2 100 0.105




e232


Table 3.6(F): Standardized Canonical Discriminant Function Coefficients (Corporate

Image)

Determinants of Corporate Image

Function

1 2 3 4 5 6

SERVICE & SUPPORT -0.862 0.679 0.221 -0.414 0.66 -0.644

COMMUNICATIONS 1.072 0.206 -0.727 -0.321 0.163 -0.332

SALES FORCE 0.167 0.114 0.642 -0.084 -1.03 0.3

PRICE 0.275 -0.337 0.649 0.137 0.406 0.834

PRODUCT -0.207 0.482 -0.236 0.937 -0.058 0.341

COMPANYBUSINESS CONDUCT 0.414 -0.582 0.572 0.412 -0.113 -0.497

Table 3.6(G): Structure Matrix (Corporate Image)

Determinants of Corporate Image Function

1 2 3 4 5 6

COMMUNICATIONS .760* 0.603 0.047 -0.074 0.189 -0.123

SERVICE & SUPPORT 0.058 .703* 0.536 -0.082 0.34 -0.304

SALES FORCE 0.264 .612* 0.503 -0.18 -0.518 0.05

PRODUCT 0.131 0.618 0.021 .772* 0.051 0.039

PRICE 0.368 0.207 0.516 -0.042 .553* 0.497

COMPANY BUSINESS CONDUCT 0.284 -0.172 0.459 0.507 0.084 -.644*

Table 3.6(H): Unstandardized Canonical Discriminant Function (Corporate Image)

Determinants of Corporate Image Function

1 2 3 4 5 6

SERVICE & SUPPORT -1.117 0.88 0.286 -0.537 0.856 -0.834

COMMUNICATIONS 1.741 0.335 -1.181 -0.522 0.264 -0.539

SALES FORCE 0.202 0.138 0.777 -0.101 -1.246 0.364

PRICE 0.367 -0.45 0.866 0.182 0.542 1.114

PRODUCT -0.352 0.818 -0.4 1.59 -0.099 0.579

COMPANY BUSINESS CONDUCT 0.442 -0.622 0.611 0.441 -0.121 -0.532

(Constant) -4.825 -4.181 -3.171 -4.683 -0.881 -0.716




e233

Table 3.6(I): Functions at Group Centroid (Corporate Image)

Bank Name Function

1 2 3 4 5 6

AXIS 0.792 -0.575 0.566 0.144 -0.1 0.169

HDFC -2.262 1.285 0.054 0.287 -0.018 0.019

ICICI 0.787 1.001 -0.313 -0.525 0.28 0.026

CITI 1.576 1.058 1.496 0.376 -0.033 -0.173

HSBC -0.18 0.36 -0.205 -1.331 -0.528 -0.063

BOB -0.924 -2.129 0.224 -0.147 0.119 -0.064

SBI 0.880 -0.231 -1.21 0.496 -0.089 -0.042

Table 3.6(J): Classification Resultsa

(Corporate Image)

Bank Name



Count

AXIS 3 0 1 0 0 1 1 6

HDFC 0 5 0 0 1 0 0 6

ICICI 0 0 2 2 1 0 1 6

CITI 0 0 0 3 0 0 0 3

HSBC 0 0 1 0 1 0 0 2

BOB 2 0 0 0 1 3 0 6

SBI 2 0 0 0 0 0 4 6

%

AXIS 50 0 16.7 0 0 16.7 16.7 100

HDFC 0 83.3 0 0 16.7 0 0 100

ICICI 0 0 33.3 33.3 16.7 0 16.7 100

CITI 0 0 0 100 0 0 0 100

HSBC 0 0 50 0 50 0 0 100

BOB 33.3 0 0 0 16.7 50 0 100

SBI 33.3 0 0 0 0 0 66.7 100





e234

Chart 3.4(A): Canonical Discriminant Functions (Determinants of Corporate Image)

Chart 3.4(B): Attribute Based Perceptual Mapping (Determinants of Corporate Image)




e235


1. Using factor analysis (Table 3.6(A)), we can find 6 different factors,

where we have divided all the activities followed by banks. In order to

streamline the process we will group all the factors in six groups given in

the determinants of corporate image model (Refer to, Chapter 1, Figure

1.4) namely F1_Service & Support, F2_Communication, F3_Sales Force,

F4_Price, F5_Product and F6_Company Business Conduct. Now we will

carry on the discriminant analysis to find out differences in various

brand management practices followed by various banks on two

dimensions.

2. In Table 3.6(F), we can find that Function-1 consist of Service & Support

and Product, where as Function-2 is consist of Communication and

Company Business Conduct. Other factors like Price and Sales Force are

not able to discriminate between two functions.

3. Looking at Table 3.6(I) and Chart 3.4(B), Function-1 consist of Axis and

SBI, Function-2 consist of HDFC and ICICI, where as we can find that

CITI bank is having strong position, on both the dimensions.

3.2.3.4: RO_04: To study the impact of brand Image on

customers while bank selection.

Looking at Table 3.7; we can find that „Safety & Security‟ scores top in the

customer‟s preferences while Bank Brand Selection, followed by Brand Image

of the Bank and Returns on Deposits. Where as we can find that „Price & Fees

of the product‟ and „Wide Range of Products and Services‟ are less important

factors for the customers while selecting a bank brand.

Table 3.7: Important Factors while Bank Selection

Factors Mean Total Score Rank

Safety & Security 4.19 4402 1

Brand/Image of the Bank 3.99 4186 2

Returns on Deposits 3.89 4082 3

Good Care by Financial Advisor 3.81 4005 4

Wide range of Products/Services 3.80 3988 5

Price and Fees of Products/Services 3.77 3958 6




e236

3.2.3.5: RO_05: To investigate the efforts put in by banks for

building their brand

The researcher asked bank mangers whether they take any steps for the brand

management practices on day to day level at branches, and the responses were as

below:

Chart 3.5: Do Branch Managers take any steps on Brand Management on day to day basis at

Branch-Level

Table 3.8(A): Steps taken by Managers for Brand Management on day to day basis at Branch-

Level

Brand Management Steps at Branch

Level/ Bank Brand



No response (blanks) 6 6 4 3 2 5 6

Cleanliness & Ambience of a Branch

, Providing Basic Information to

Customers

0 0 1 0 0 1 0

Personal Self - Grooming, Greeting

and thanking the Customers 0 0 1 0 0 0 0

Total Response 6 6 6 3 2 6 6

Table 3.8(B): New Product Developments

What products your bank has

developed to give service

differentiation to clients in recent

years?

Bank Name



No response (Blanks) 3 2 3 2 1 0 4

Net banking for all segment of

customers 0 1 0 0 0 1 0

Mobile Banking 0 0 0 1 0 0 0




e237

Tele-Banking 0 0 0 0 0 1 0

Baroda Nxt 0 0 0 0 0 2 0

Free debit/credit card 1 0 0 0 0 3 0

Classifying customers into different

segments 0 1 0 0 0 0 0

Door to Door Banking 0 3 0 0 0 0 0

Liability Products 0 2 0 0 0 0 0

Service Quality for Customers 0 2 1 0 0 0 0

Club SD 1 0 0 0 0 0 0

Corporate Banking 0 0 0 0 0 0 1

Family Banking 0 0 1 0 0 0 0

Global Banking 0 0 0 0 1 0 0

HOME LOANS 0 0 1 0 0 0 0

House Loans/ Car Loans 0 0 0 0 0 0 1

SMT A/c 2 0 0 0 0 0 0

TCDC 1 0 0 0 0 0 0

FIXED DEPOSITS 0 0 1 0 0 0 0

Fix Rate of Interest 0 0 0 0 0 0 1

General Insurance 0 0 0 0 0 0 1

Group Accounts 0 0 1 0 0 0 0

Me to Me transfer 0 0 0 0 1 0 0

Priority A/C 1 0 0 0 0 0 0

Freeze Period Loans 0 0 0 0 0 0 1

SBI Life Insurance 0 0 0 0 0 0 1

Variety of Products 0 0 1 0 0 0 0


Table 3.8(C): Efforts Put In By Bank To Enhance Its Brand Image In Recent Times

What steps your bank has taken to enhance the Brand

Image of your Bank in recent times?

Bank Name



1 No response (Blanks) 2 0 3 2 1 3 3

Advertisement 1 0 0 0 0 0 0

Arranging Various Events 0 0 1 0 0 0 0

Cross Selling 0 0 0 0 0 0 1

Customer Services 0 2 0 0 0 0 1

Focused on technology Driven Models 1 0 0 0 0 0 0

Improved Service 1 0 0 0 0 0 0

Introduction of Latest Technology 0 0 0 0 0 1 0

Maintain Quality 1 0 0 0 0 0 0

Marketing It Strongly 0 0 0 0 1 0 0

Marketing of our Bank Brand on regular basis 0 1 0 0 0 0 0

New Logo Development 0 0 0 0 0 2 0

Promote services at its best 0 1 0 0 0 0 0

Quick Services by Less Documentation Process 0 0 0 0 0 0 1




e238

RETAINED ITS PEOPLE IN TIMES OF

ECONOMIC RECESSION. FIRED THEM AFTER

YEARLY RATING.

0 0 1 0 0 0 0

Stay in touch with all the customers 0 0 0 1 0 0 0

That can been seen by the change and sustainability of

bank form last year crisis

0 0 1 0 0 0 0

TV Advertisements 0 2 0 0 0 0 0


Baroda Nxt Branding 0 0 0 0 0 2 0

Collecting regular feedbacks for customers 0 0 0 1 0 0 0

Guide Customers for all the services available under

one roof

0 0 0 0 0 0 1

Mauritius Scholarships 0 2 0 0 0 0 0

Online banking 0 0 0 0 0 1 0

Productivity 0 0 0 0 0 0 1

Senior Citizen Day 0 0 1 0 0 0 0

Separate Products for HNIs 0 0 0 0 0 0 1


24*365 Banking in Certain Branches 0 0 0 0 0 1 0

2M Offer 0 2 0 0 0 0 0

Centralized Help Desk 0 0 0 0 0 0 1

Drawing Competition For Children 0 0 1 0 0 0 0

Punctuality in services 0 0 0 0 0 0 1


BPR Initiative 0 0 0 0 0 0 1



1. Looking at chart 3.5, we can see that 80 percent of the managers replied that

they don‟t take any steps towards „brand management practices on day-to-

day basis at branch level. Only 20 percent of the managers follow the

branding practices on day-to-day basis. The steps followed by these

managers are available in Table 3.8(A).

2. When the researcher asked a question “What Products your Bank has

developed to give service differentiation to clients in recent years” (Table

3.8(B)) was asked to the Branch Mangers the researcher came to know the

following facts:

3. The researcher came to know that SBI Life Insurance is one of the successful

product in the market that is been developed to meet the changing needs of

the State Bank of India‟s Customers. Door to Door banking in HDFC, Global




e239

Banking in HSBC, and Baroda Nxt in Bank of Baroda. Priority Banking in

Axis Bank, etc.

3.2.3.6: RO_06: To find out the factors that helps customer

banking with his/her bank.

In order to find the factors which are important to the customers, the researcher

has asked the question to customers that 'what are the important reasons behind

banking with his/her bank'. Here he found that Past Experience has got the

highest communalities value of 0.572. We will now try to reduce the number of

variables using factor analysis and multidimensional scaling techniques.

A. Factor Analysis:

Table 3.9(A): Communalities (Factors for Banking with Existing Bank)

Factors Initial Extraction

Past Experience 1 0.572

Price 1 0.47

Personal Recommendation 1 0.322

Quality 1 0.38

Well Known/Advertised 1 0.407

Ratings in Consumer Report 1 0.542


Total Variance: 44.86%; KMO: 0.586


Table 3.9(B): Component Matrixa (Factors for Banking with Existing Bank)

Factors Component

1 2

Past Experience 0.216 0.725

Price 0.603 0.327

Personal Recommendation 0.46 -0.332

Quality 0.463 0.406

Well Known/Advertised 0.552 -0.32

Ratings in Consumer Report 0.662 -0.32


a. 2 components extracted.




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Table3.9(C): Rotated Component Matrixa

(Factors for Banking with Existing Bank)

Factors Component

1 2

Past Experience -0.249 0.714

Price 0.298 0.618

Personal Recommendation 0.567 0

Quality 0.138 0.6

Well Known/Advertised 0.635 0.063

Ratings in Consumer Report 0.725 0.127


Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 3 iterations.


It is evident from Table 3.9(A) (looking at Total Variance); we find that the two

factors extracted together account for 44.86% of the total variance. Hence we

have reduced the number of variables from six to two factors.

In general any variable with loadings of 0.30 or higher is considered to be

statistically significant (Hair1 et al., 1998).

Factor 1:

Looking at Table 3.9(B) & Table 3.9(C) we see that the variables „Well

Known/Advertised‟ (0.635-Table 3.9(C), 0.552-Table 3.9(B)) and „Ratings in

Consumer Report‟ (0.725-Table 3.9(C), 0.662-Table 3.9(B) have higher

loadings. There for the Factor 1 is interpreted as Bank‟s External

Communication plays vital role in customer‟s preference for banking with that

bank.

Factor 2:

Looking at Table 2 & Table 3, we see that the variables „Quality‟ (0.600-Table

3.9(C), 0.406-Table 3.9(B)) and „Price‟ (0.618-Table 3.9(C), 0.327-Table

3.9(B)) have higher loadings. There for the Factor 2 is interpreted as Bank‟s

1 Hair J F, Anderson R E, Tatham R L and Black W C (1998), Multivariate Data Analysis, 5th Ed.,

Prentice-Hall, NJ.




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Internal Communication (Quality & Price) plays vital role in customer‟s

preference for banking with that bank.

The same can be interpreted form the Table 3.9(A), where the high

communality score shows the high dependability of the respective variables.

B. Regression analysis:

Customers were asked to give their response on the multiple-choice question

(What are your main reasons behind banking with this bank?) with below given

option.

We want to use the regression model for finding out the Loyalty Rating of

customers using 6 service variables.

Dependent Variables:

Y= Loyalty: - I consider myself to be loyal customer of this bank (Q.1)

Independent Variables:

The Independent Variables are mentioned in Exhibit 3.2

Exhibit 3.2: Factors for Banking with Existing Bank

VAR.CODE VAR. VALUES

X1= Past Experience

X2= Price

X3= Personal Recommendation

X4= Quality

X5= Well Known/Advertised

X6= Ratings in consumer report

I. Correlation among Variables:

First let us look at the correlations of all the variables with each other. The

correlation table (using Pearson Correlation Procedure) is shown in Table

3.9(D). The value in the correlation table is standardized, and range from 0 to

1 (positive and negative). All the variables are moderately correlated with

Loyalty; Where Quality and Well known/Advertised shows high correlation

with Loyalty. That means we may have chosen a fairly good set of




e242

independent variables. But we must remember that these correlations in Table

3.9(D) are one-to-one correlations of each variable with each other. So we

may still want to do a multiple regression with an independent variable

showing low correlation with a dependent variable, because in presence of

other variables, this independent variable may become a good predictor of the

dependent variable.

Table 3.9(D): Correlation (Factors for Banking with Existing Bank)

Past

Experience Price

Personal

Recommendation Quality

Well

Known/Advertise

d

Loyalty

Past Experience 1 .140** -0.036 .084** 0.032 -0.011

Price 1 .175** .181** .103** -.079*

Personal

Recommendation 1 0.028 .086** -0.02

Quality 1 0.059 0.024

Well

Known/Advertised 1 0.014

Loyalty 1

II. Multiple Regression with All Variables:

We will first run the regression model of the following form, by entering all

the 10 „x‟ variables in the model.

Y = a + b1X1+ b2X2+ b3X3+ b4X4+ b5X5+ b6X6

(Equation 1)

The results of this regression model are in Table 3.9(E), column no.2; titled

„B‟ lists all the coefficients for the model. According to this,

a (constant) =4.106

b1= -0.009

b2= -0.228

b3= -0.009

b4= 0.096

b5= 0.086

b6= -0.118

These values can be substituted in the Equation 1 above and we can write,




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Loyalty = 4.106 – 0.009 (Past Experience) - 0.228 (Price) – 0.009 (Personal

Recommendations) + 0.096 (Quality) + 0.086 (Well known/Advertised) -

0.118 (Ratings in Consumer Report)

Before we use this equation, however, we need to look at the statistical

significance of the model and the R-square value. These are available from

Table 3.9(E). The analysis of variance Table 3.9(F), the last column indicates

the p-level to be 0.000, which indicates the significance of the F value. We

also know that t-test for significance of individual independent variables

indicates that at the significance level of 0.10, only Price is statistically

significant in the model. The other 5 independent variables are individually

not significant. But we can follow either the Forward or Backward stepwise

Regression method to tray and eliminate the „insignificant‟ variables from the

full regression model containing all 6 independent variables.

III. Forward Stepwise Regression:

We could use the forward stepwise regression model to reduce no of variables

effecting customer loyalty. Before we proceed for further analysis the Table

3.9(E) shows a significance value of 0.011(Model 6), which means the model

is statistically significant at 89% of confidence level. Now looking at the

Table 3.9(E), we get the output with reduced number of variables from 6

variables to only 1 „Price‟. Even the ANOVA Value in Table 3.9(F) shows

statistically significant value of 0.011 for the forward stepwise regression

model.

If we decide to use the model from Table 3.9(G), it would be written as

follows:

Loyalty = 4.134- 0.216 (Price)

(Equation 2)




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But researcher believes that „price‟ only will not give us a right customer

loyalty rating, so we use model no 3., with significance level of 0.046 (Table

3.9(G), last column), now we can write the refined equation with all the

necessary variables as (Table 3.9(G), Model 3):

Loyalty = 4.100 – 0.231 (Price) + 0.096 (Quality) + 0.085 (Well

Known/Advertised) – 0.119 (Ratings in consumer Report)

Similarly we could use this model to make predictions regarding Loyalty for

which Trust, Staff, Quality and Innovative rating is known.

Say for example is Price=0.30, Quality = 0.096, Well Known/Advertised =

0.50, Ratings in Consumer Report = 0.25 than the Loyalty would be:

Loyalty= 4.100 – 0.231(0.30) + 0.096 (0.50) + 0.085 (0.50) – 0.119 (0.25)

Loyalty = 4.0914.

This value indicates, Strong Loyalty Level by the customer.

Table 3.9(E): Model Summaryg (Factors for Banking with Existing Bank)

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .096a 0.009 0.004 1.094

2 .096b 0.009 0.004 1.093

3 .096c 0.009 0.005 1.093

4 .092d 0.008 0.006 1.093

5 .088e 0.008 0.006 1.092

6 .079f 0.006 0.005 1.093

Dependent Variable: I consider myself to be a loyal customer of this bank

Table 3.9(F): ANOVAg (Factors for Banking with Existing Bank)

Model Sum of Squares df Mean Square F Sig.

1 Regression 11.622 6 1.937 1.619 .138a

Residual 1247.601 1043 1.196

Total 1259.223 1049

2 Regression 11.605 5 2.321 1.942 .085b

Residual 1247.618 1044 1.195

Total 1259.223 1049

3 Regression 11.585 4 2.896 2.426 .046c

Residual 1247.638 1045 1.194

Total 1259.223 1049

4 Regression 10.567 3 3.522 2.951 .032d




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Residual 1248.656 1046 1.194

Total 1259.223 1049

5 Regression 9.683 2 4.842 4.057 .018e

Residual 1249.539 1047 1.193

Total 1259.223 1049

6 Regression 7.777 1 7.777 6.513 .011f

Residual 1251.445 1048 1.194

Total 1259.223 1049


Table 3.9(G): Coefficientsa:

Variables in the equation (Factors for Banking with

Existing Bank)

Model Unstandardized

Coefficients

Standardized

Coefficients

T Sig.

B Std. Error Beta

1 (Constant) 4.106 0.059 69.93 0

Past Experience -0.009 0.069 -0.004 -0.137 0.891

Price -0.228 0.089 -0.083 -2.568 0.01

Personal

Recommendation

-0.009 0.075 -0.004 -0.119 0.905

Quality 0.096 0.071 0.043 1.351 0.177

Well Known/Advertised 0.086 0.092 0.03 0.929 0.353

Ratings in Consumer

Report

-0.118 0.112 -0.035 -1.052 0.293

2 (Constant) 4.103 0.055 74.667 0

Past Experience -0.009 0.068 -0.004 -0.13 0.897

Price -0.23 0.088 -0.084 -2.622 0.009

Quality 0.097 0.071 0.043 1.354 0.176


Ratings in Consumer

Report

-0.12 0.111 -0.035 -1.076 0.282

3 (Constant) 4.1 0.047 88.059 0

Price -0.231 0.087 -0.084 -2.662 0.008

Quality 0.096 0.071 0.043 1.349 0.178


Ratings in Consumer

Report

-0.119 0.111 -0.035 -1.073 0.284

4 (Constant) 4.11 0.045 91.129 0

Price -0.226 0.087 -0.082 -2.61 0.009

Quality 0.097 0.071 0.043 1.358 0.175

Ratings in Consumer

Report

-0.092 0.107 -0.027 -0.86 0.39

5 (Constant) 4.104 0.045 92.134 0

Price -0.236 0.086 -0.086 -2.739 0.006

Quality 0.089 0.071 0.04 1.264 0.207

6 (Constant) 4.134 0.038 109.78 0

Price -0.216 0.085 -0.079 -2.552 0.011

a. Dependent Variable: I consider myself to be a loyal customer of this bank





e246

3.2.3.7: OB7: To find out the various effective Advertising

media noticed by the customers

The researcher asked a question to the customers, ‟where they find the

advertisements of their bank brands?‟ Here looking at Table 3.10(A), we can

find that Television got the first rank with total score of 704, followed by News

Paper and Outdoor Media as per the answers given by customer. Due to recent

trends in the radio industry, it is now also famous among customer as an

advertising media. Due to the advancements in Telecom industry and low cost

of SMS, it has been immerged as a vital media for advertising.

Table 3.10(A): Effective Advertising Media

Media/Medium Total Score Rank

T.V. 704 1

NEWS PAPER 677 2

HORDINGS/BANNERS 443 3

MAGAZINE 381 4

INTERNET/WEB 377 5

SMS 224 6

TELLY-CALLING 192 7

WALL PAINTINGS 167 8

BUS PAINTINGS 159 9

RADIO 124 10

OTHERS 39 11

EVENTS 16 12

POINT OF PURCHASE MATERIALS 11 13

COMPANY BROCHURE 7 14

A. Factor Analysis:

Table 3.10(B) KMO and Bartlett's Test (Effective Advertising Media)


Bartlett's Test of

Sphericity

Approx. Chi-Square 2432.955

df 91

Sig. 0

Total Variance Explained 51.32%




e247

Table 3.10(C): Items, Loading and Communalities (Rotated Component Matrixa)-

(Effective Advertising Media)

Sr. No.

COMPONENTS Commun

alities

Component

F1 F2 F3 F4

F1: TRADITIONAL

MEDIA

1 NEWS PAPER 0.467 0.675 0.081 -0.027 -0.057

2 MAGAZINE 0.533 0.669 0.258 0.137 -0.028

3 T.V. 0.409 0.629 0.083 -0.066 -0.047

4 HORDINGS/BANNER

S

0.408 0.578 0.183 0.131 0.151

5 INTERNET/WEB 0.489 0.603 0.220 0.235 0.149

F2: OUTDOOR

MEDIA

6 WALL PAINTINGS 0.544 0.295 0.619 0.265 0.056

7 BUS PAINTINGS 0.599 0.234 0.735 0.058 -0.002

8 RADIO 0.373 0.272 0.524 0.153 -0.020

F3:

TECHNOLOGICAL

MEDIA

9 SMS 0.557 0.301 0.257 0.631 -0.04

10 TELLY-CALLING 0.575 0.214 0.352 0.631 -0.084

11 OTHERS 0.513 -0.277 -0.12 0.616 0.204

F4: INTERNAL

MEDIA

12 COMPANY

BROCHURE

0.534 0.044 0.153 -0.004 0.713

13 EVENTS 0.595 0.114 -0.29 0.168 0.685

14 POINT OF

PURCHASE

MATERIALS

0.591 -0.222 0.479 -0.233 0.507


Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 10 iterations.



The KMO measure with value of 0.830 (Table 3.10 (B)) for the above

mentioned (Table 3.10(A)) scaling item, shows a better reliability of the scale.

Now we will further proceed with factor analysis using these scaling variables.

And we can find out four major components of the scale which, the researcher

has tried to group, with names like F1: TRADITIONAL MEDIA, F2:




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OUTDOOR MEDIA, F3: TECHNOLOGICAL MEDIA AND F4: INTERNAL

MEDIA (Table 3.10(C)).

In the F1: TRADITIONAL MEDIA, the new paper got the highest loading

value of 0.675 and the same way all the factors have sub components with

different loadings.

At last, we can conclude that these four are the majority type of media which

are regularly noticed by the banking customers.

3.2.3.8: RO_08: To find out the various factors, that customers

are looking for in a bank brand.

A. Reliability of a scale:

Mainly, reliability is a measure of how a scale can be relied on to produce

similar measurements every time we use the scale. We will use Cronbach‟s

alpha to find out the reliable scale for customers choice while bank selection.

And later we will carry out the factor analysis to find the impact of each factor

in detail.

If the alpha value or the scale is 0.7 or more, it is usually considered a good

scale. Looking at Table 3.11(A), we can find our alpha value is 0.810, which

shows a good scale. If the item to total correlation is low for an item, we can

consider dropping the item from the scale. We can also take this decision

based on a look at the alpha value after dropping an item. If the alpha value is

high even after dropping a particular item, we can drop the item.

Form the same Table 3.11(A) we can find the reliability of a scale by

Cronbach‟s Alpha based on standardized item is 0.811. If we go further and

look at Item-Total Correlation (Table 3.11 (D)) we can find that brand image

of the bank (0.545) plays a least important role in the total scale where as

Returns on Deposit (0.606) followed by Safety & Security (0.585), plays the

vital role in the model. However it is not good to drop any of the items as




e249

more or less there is very much less difference in the items reliability and has

less effect on the Alpha value if we consider dropping any one of them. And

we conclude that all the items together play an important role while bank

selection in to the minds of customer.

Table 3.11(A): Reliability Statistics (Important Factors of a Bank Brand)

Cronbach's Alpha Cronbach's Alpha Based on Standardized

Items N of Items

0.81 0.811 6

Table 3.11(B): Inter-Item Correlation Matrix (Important Factors of a Bank Brand)

Correlation

Good

Care by

Financial

Advisor

Price and Fees of

Products/Services

Brand/Image

of the Bank

Wide range of

Products/Services

Safety &

Security

Returns

on

Deposits

Good Care by

Financial Advisor

1 0.564 0.383 0.388 0.317 0.409

Price and Fees of

Products/Services

1 0.336 0.331 0.354 0.424

Brand/Image of

the Bank

1 0.426 0.482 0.381

Wide range of

Products/Services

1 0.467 0.464

Safety & Security 1 0.521

Returns on

Deposits

1


Table 3.11(C): Summary Item Statistics (Important Factors of a Bank Brand)

Mean Minimum Maximum Range Maximum /

Minimum

Variance N of

Items

Item Means 3.908 3.77 4.192 0.423 1.112 0.026 6

Item

Variances

1.288 1.164 1.465 0.301 1.258 0.014 6

Table 3.11(D): Item-Total Statistics (Important Factors of a Bank Brand)

Factors

Scale

Mean if

Item

Deleted

Scale

Variance if

Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

Good Care by

Financial Advisor

19.63 16.703 0.567 0.393 0.781

Price and Fees of

Products/Services

19.68 17.038 0.553 0.376 0.784

Brand/Image of the

Bank

19.46 17.669 0.545 0.322 0.786

Wide range of 19.65 17.295 0.566 0.34 0.781




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Products/Services

Safety & Security 19.26 17.361 0.585 0.399 0.777

Returns on Deposits 19.56 16.79 0.606 0.391 0.772

B. Factor Analysis:

Table 3.11(E): KMO and Bartlett's Test (Important Factors of a Bank Brand)


Bartlett's Test of Sphericity Approx. Chi-Square 1839.297

df 15

Sig. 0

Total Variance Explained 51.41%

Table 3.11(F): Communalities (Important Factors of a Bank Brand)

Factors Initial Extraction

Good Care by Financial Advisor 1 0.502

Price and Fees of Products/Services 1 0.483

Brand/Image of the Bank 1 0.484

Wide range of Products/Services 1 0.513

Safety & Security 1 0.539

Returns on Deposits 1 0.563


Table 3.11(G): Component Matrixa (Important Factors of a Bank

Brand)

Factors Component

1

Good Care by Financial Advisor 0.708

Price and Fees of Products/Services 0.695

Brand/Image of the Bank 0.696

Wide range of Products/Services 0.716

Safety & Security 0.734

Returns on Deposits 0.751

Extraction Method: Principal Component Analysis. a. 1 components extracted.




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1. We have already found that the reliability of all items are important in

KMO Measures, Again looking at the Factor Analysis (Table 3.11(G)), we

can see that factor model was able to identify only one factor component

for the above-mentioned variables. This again proves that more or less

these all variables are having equal importance while selection of a bank.

2. Table 3.11(E) shows a high KMO value of 0.821, which proves the model,

is statistically significant and high reliability of a scale.

3. Looking at Table 3.11(F) and Table 3.11(G), we can conclude that, Returns

on Deposit has got the highest loading values followed by Safety &

Security, Wide range of products/services, good care by financial advisor,

brand image of the bank and at last price and fees of products and services.

4. So we can conclude price and fees of the products does is not important for

the bank customer but other factors in bank‟s arm are like good care by

financial advisor, brand image of the bank, and wide range of

products/services.

3.2.3.9: RO_09: To find out the Various Brand Building Practices that is

important to customers.

The researcher has tried to find the preferences of the customer for various

'brand building practices', and using below given models we will be able to find

out the answers to our questions:

A. Regression Analysis:

Customers were asked to give their response on the 5-point likert scale, with 5

representing „strongly agree‟ and 1 as „strongly disagree‟.

We want to use the regression model for finding out the satisfaction level of

customers using 8 to 10 service variables.

Dependent Variables:

Y= Loyalty: I consider myself to be loyal customer of this bank (Q1)




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Independent Variables (refer to Exhibit 3.3):

Exhibit 3.3: Brand Loyalty Variables

VAR.CODE VAR. Title VAR. VALUES (Questions)

X1= Trust My bank is... Trustworthy

X2= Promotion My bank... Offers fun promotion

X3= Quality My bank... Has high quality Services

X4= Variety My bank... Has variety of services

X5= Staff I am satisfied with... Staff Response at my bank

branch

X6= Website I like to visit website of my bank

X7= Location My Bank is easily reachable

X8= Timings Branch Timings of Bank

X9= Merchandise I would be interested in merchandise with my bank

name on it

X10= Innovative My bank is... Innovative

I. Correlation:

First let us look at the correlations of all the variables with each other.

The correlation table (using Pearson Correlation Procedure) is shown in

Table 3.12(A). The value in the correlation table is standardized, and

range from 0 to 1 (positive and negative). All the variables are

moderately correlated with sales except Trust, Quality; Staff and

Innovative shows high values of correlation. That means we may have

chosen a fairly good set of independent variables. But we must remember

that these correlations in Table 3.12(A) are one-to-one correlations of

each variable with the other. So we may still want to do a multiple

regression with an independent variable showing low correlation with a

dependent variable, because in presence of other variables, this

independent variable may become a good predictor of the dependent

variable.




e253

Table 3.12(A): Correlation (Brand Loyalty Factors)

Correlation X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 Y

X1 1 0.219 0.322 0.327 0.256 0.22 0.365 0.164 0.258 0.368 0.310

X2 1 0.381 0.294 0.282 0.278 0.181 0.117 0.262 0.333 0.177

X3 1 0.35 0.463 0.34 0.261 0.255 0.326 0.391 0.290

X4 1 0.294 0.251 0.286 0.197 0.303 0.335 0.189

X5 1 0.278 0.268 0.351 0.285 0.262 0.274

X6 1 0.233 0.283 0.456 0.327 0.203

X7 1 0.267 0.231 0.251 0.196

X8 1 0.262 0.238 0.111

X9 1 0.311 0.171

X10 1 0.246

Y 1


II. Multiple Regression with All Variables:

Regression:

We will first run the regression model of the following form, by entering

all the 10 „x‟ variables in the model

Y = a + b1X1+ b2X2+ b3X3+ b4X4+ b5X5+ b6X6 + b7X7+ b8X9+

b9X9+ b10X10 (Equation 1)

Regression Output:

The results of this regression model are in Table 3.12 (C), column no.2, of

the table titled „B‟ lists all the coefficients for the model. According to

this,

a (constant) =1.815

b1= 0.213

b2= 0.009

b3= 0.109

b4= 0.003

b5= 0.133

b6= 0.055

b7= 0.036

b8= -0.044




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b9= -0.006

b10= 0.079

These values can be substituted in the Equation 1 above and we can write,

Loyalty = 1.815+ 0.213 (Trust) + 0.009 (Promotion) + 0.109 (Quality) +

0.003 (Variety) + 0.133 (Staff) + 0.055 (Website) + 0.036 (Location) -

0.044 (Timings) - 0.006 (Merchandise) + 0.079 (Innovative)

Before we use this equation, however, we need to look at the statistical

significance of the model and the R-square value; these are available from

Table 3.12(C), last row. The analysis of variance Table 3.12(B), the last

column indicates the p-level to be 0.000 which indicates the significance

of the F value. We also not that t-test for significance of individual

independent variables indicate that at the significance level of 0.10, only

Trust, Quality, Staff, Website and Innovative are statistically significant in

the model. The other 5 independent variables are individually not

significant. But we can follow either the Forward or Backward stepwise

Regression method to tray and eliminate the „insignificant‟ variables from

the full regression model containing all 10 independent variables.

Table 3.12(B) ANOVAb

(Brand Loyalty Factors)

Model Sum of Squares Df Mean Square F Sig.

1

Regression 206.842 10 20.684 20.421 .000a

Residual 1052.381 1039 1.013

Total 1259.223 1049


Table 3.12(C) Coefficientsa (Brand Loyalty Factors)

Model Unstand. Cof. Stand. Cof. Of t-Val Sig.

B Std. Error Beta

1 (Constant) 1.815 0.185 9.818 0

Trust 0.213 0.037 0.189 5.738 0

Promotion 0.009 0.029 0.01 0.314 0.754

Quality 0.109 0.035 0.112 3.14 0.002

Variety 0.003 0.033 0.003 0.101 0.919

Staff 0.133 0.032 0.141 4.133 0




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Website 0.055 0.03 0.062 1.848 0.065

Location 0.036 0.031 0.038 1.179 0.239

Timings -0.044 0.033 -0.043 -1.362 0.174

Merchandise -0.006 0.032 -0.006 -0.191 0.849

Innovative 0.079 0.036 0.074 2.186 0.029


R Value: 0.405, R-Square 0.164, Adjusted R Square: 0.156

III. Forward Stepwise Regression:

We have used the forward stepwise regression model to reduce no of

variables effecting customer loyalty. Before we proceed for further

analysis the Table 3.12(E) shows a significance value of 0.000, which

means the model, is statistically significant. Now looking at the Table 3.12

(F), we got the output with reduced number of variables from 10 variables

to only 4 like Trust, Staff, and Quality and innovative. Even the ANOVA

Value in Table 3.12 (E) shows statistically significant value of 0.000 for

the forward stepwise regression model.

If we decide to use the model from Table 3.12 (F), it would be written as

follows:

Loyalty = 1.849+ 0.228 (Trust) + 0.135 (Staff) + 0.122 (Quality) + 0.091

(Innovative)

(Equation 2)

Similarly we could use this model to make predictions regarding Loyalty

for which Trust, Staff, Quality and Innovative rating is known.

Say for example is Trust=4, Staff = 4, Quality = 5 and Innovative =3 than

the Loyalty would be:

Loyalty= 1.849+ 0.228 (4) + 0.135 (4) + 0.122 (5) + 0.091 (3)

Loyalty = 4.184.

This shows, Strong Loyalty Level by the customer.

Whereas, in the step wise regression model we can find that Trust alone

has came with Highest „B‟ value of 0.350, which indicates only trust

factor in the bank brand can help in predicting the customer loyalty.




e256

Table 3.12(D) Model Summary (Brand Loyalty Factors)

Model R R Square Adjusted

R Square

Std. Error

of the

Estimate

1 .310a 0.096 0.095 1.042

2 .370b 0.137 0.135 1.019

3 .392c 0.153 0.151 1.01

4 .399d 0.159 0.156 1.007

Table 3.12(E): ANOVAe (Brand Loyalty Factors)

Model Sum of Squares Df Mean Square F Sig.

1 Regression 121.084 1 121.084 111.495 .000a

Residual 1138.138 1048 1.086

Total 1259.223 1049

2 Regression 172.351 2 86.176 83.014 .000b

Residual 1086.872 1047 1.038

Total 1259.223 1049

3 Regression 193.026 3 64.342 63.123 .000c

Residual 1066.196 1046 1.019

Total 1259.223 1049

4 Regression 200.17 4 50.043 49.379 .000d

Residual 1059.053 1045 1.013

Total 1259.223 1049


Table 3.12(F) Coefficientsa (Brand Loyalty Factors)


Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 2.644 0.141 18.785 0

Trust 0.350 0.033 0.31 10.559 0

2 (Constant) 2.156 0.154 13.981 0

Trust 0.290 0.034 0.257 8.646 0

Staff 0.198 0.028 0.209 7.028 0

3 (Constant) 1.997 0.157 12.738 0

Trust 0.253 0.034 0.224 7.405 0

Staff 0.14 0.031 0.148 4.574 0

Quality 0.145 0.032 0.149 4.504 0

4 (Constant) 1.849 0.166 11.145 0




e257

Trust 0.228 0.035 0.202 6.438 0

Staff 0.135 0.031 0.142 4.4 0

Quality 0.122 0.033 0.125 3.674 0

Innovative 0.091 0.034 0.085 2.655 0.008


3.2.3.10: RO_10: To find out the customer’s perception for

various banking sector.

We will now draw a perceptual map using an attribute-based procedure, of its

consumer‟s perceptions regarding its own band competing brands. Perceptual

maps can be best seen on two dimensions, for that we have to recode our 7 bank

brands in to three groups, So that we can have two dimensions in the model.

We will now recode our existing variable Bank Code in to new variable Bank

Sector (refer to, Exhibit 3.4).

Exhibit 3.4: Sector Wise Grouping of Banks

Old Variable New Variable

1= AXIS

1 = Private Sector 2= HDFC

3 = ICICI

4= CITI 3= Foreign Sector

5 = HSBC

6= BOB 2= Public Sector

7 = SBI

I. Attribute Based Perceptual Mapping using Discriminant Analysis:

Table 3.13(A), Table 3.13(B), Table 3.13(I), show the output of the

Discriminant analysis from SPSS using the input data. Chart 3.6, is a part of the

SPSS Discriminant analysis output. As there are three brands we can get the

maximum two dimensions.

Table 3.13(A): Group Statistics (Perception for various banking sector)

Bank Sector Bank Brand

Characteristics

Valid N (list wise)

Mean Std.

Deviation Unweighted Weighted

Private

Sector

Innovative 3.93 1.08 525 525

Knowledgeable 3.95 1.107 525 525




e258

Trustworthy 4.09 0.97 525 525

Likeable 3.88 1.022 525 525

Admirable 3.78 1.068 525 525

Public Sector

Innovative 3.68 1.018 350 350


Trustworthy 4.26 0.932 350 350

Likeable 3.82 1.043 350 350

Admirable 3.69 1.144 350 350

Foreign

Sector

Innovative 4.23 0.738 175 175


Trustworthy 4.04 1.03 175 175

Likeable 3.98 0.78 175 175

Admirable 3.91 0.928 175 175

Total

Innovative 3.9 1.026 1050 1050

Knowledgeable 3.93 1.068 1050 1050

Trustworthy 4.14 0.971 1050 1050

Likeable 3.88 0.994 1050 1050

Admirable 3.77 1.074 1050 1050


Table 3.13(B): Eigenvalues (Perception for various banking sector)


1 .067a 98.5 98.5 0.25

2 .001a 1.5 100 0.031


Table 3.13(C): Wilks' Lambda (Perception for various banking sector)

Test of Function(s) Wilks' Lambda Chi-square Df Sig.

1 through 2 0.937 68.556 10 0

2 0.999 1.032 4 0.905

Table 3.13(D): Standardized Canonical Discriminant Function Coefficients

(Perception for various banking sector)

Function

Brand Perception Characteristics 1 2

Innovative 0.814 0.395

Knowledgeable 0.250 0.030

Trustworthy -0.851 0.828

Likeable 0.154 -0.379

Admirable 0.028 0.239




e259

Table 3.13(E): Structure Matrix (Perception for various banking sector)

Brand Perception Characteristics

Function

1 2

Innovative .704* 0.672

Trustworthy -0.334 .890*

Knowledgeable 0.418 .566*

Admirable 0.262 .488*

Likeable 0.2 .358*

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions, Variables ordered by absolute size of correlation within function.

*. Largest absolute correlation between each variable and any discriminant function

Table 3.13(F): Unstandardized Canonical Discriminant

Function Coefficients (Perception for various banking sector)

Function

1 2

Innovative 0.806 0.391

Knowledgeable 0.235 0.028

Trustworthy -0.879 0.856

Likeable 0.155 -0.382

Admirable 0.026 0.223

(Constant) -1.131 -4.531

Table 3.13(G): Functions at Group Centroids


Bank Sector Function

1 2

Private Sector 0.073 -0.03

Public Sector -0.32 0.021

Foreign Sector 0.422 0.048





e260

Table 3.13(H): Prior Probabilities for Groups (Perception for various banking sector)

Bank Sector Cases Used in Analysis

Prior Unweighted Weighted

Private Sector 0.333 525 525

Public Sector 0.333 350 350

Foreign Sector 0.333 175 175

Total 1 1050 1050

Table 3.13(I): Classification Resultsa


Bank Sector

Predicted Group Membership

Private Sector Public Sector Foreign

Sector Total

Original

Count

Private Sector 96 184 245 525

Public Sector 69 196 85 350

Foreign Sector 43 51 81 175

%

Private Sector 18.3 35 46.7 100

Public Sector 19.7 56 24.3 100

Foreign Sector 24.6 29.1 46.3 100



Chart 3.6: Attribute based Perceptual Map of different Bank Sectors




e261


1. In this case the, interpretation in terms of the variables and their

correlation to dimensions 1 and 2 can be found from Chart 3.6. This has

been drawn separately on excel using the data from Table 3.13(D)

(standardized Discriminant function coefficients of each attribute on

each function) and Table 3.13(G) (Centroids for each bank sector or

group). The two Discriminant functions represent the two axes or

dimensions in Chart 3.6.

2. As seen from the perceptual map in Chart 3.6, Private Public and

Foreign Sector, the three sectors, have their unique positions on the

map. In addition, on the same map, we have plotted value of the

attributes on the same two dimensions (each Discriminant function

represents a dimension). As we can see, Dimension 1 seems to

comprise Innovative (0.814) and Knowledgeable (0.250), where as

Dimension 2 seems to comprise of Trustworthy (0.828) and

Admirable (0.239) (Value are in Table 3.13(D)) but „Likeable‟ is not

the important attribute in differentiating between three banking sectors.

That is also evident from its low score on Table 3.13(D).

3. Looking at Chart 3.6, we can find that Foreign Sector Banks seems to

be stronger on dimension 1 (Innovative and Knowledgeable) and Public

Sector Banks seems to be stronger on dimension 2 (Trustworthy and

Admirable).

4. If we still, want to refine our statement and want to find clear

discrimination between all the three sectors than we can say Foreign

Sector Banks are strong in Innovative attribute, Public Sector Banks

seems to be strong in Trust Attribute, and Indian Private Sector Banks

are having an equal balance on the Innovative and Trust attributes.

5. At last we conclude that customer‟s perception for various banking

sectors does differ.




e262

3.2.3.11: RO_11: To find out the Customer’s Perception for various banks

of private sector

We will now draw a perceptual map using an attribute-based procedure, of its

consumer‟s perceptions regarding its own band competing brands. Perceptual

maps can be best seen on two dimensions, for that we can use only three banks

at a time. Now we can have two dimensions in the model. We will try to find

out the perception of consumers on 7Ps of marketing regarding their bank.

Table 3.14(A): 7ps of Service Marketing (Perception for Various Banks)

Statement Regarding

Bank Services on 7ps

Bank Name



Product: 3.83 3.92 3.78 3.95 3.91 3.70 3.76

I will use other

products/services offered

by this bank in near future

3.86 3.97 3.73 3.80 3.59 3.70 3.95

Innovative 3.88 4.06 3.85 4.02 4.51 3.70 3.71

Knowledgeable 3.87 3.99 3.98 4.07 4.25 3.90 3.74

Is for whole family 3.66 3.65 3.45 3.96 3.31 3.70 3.61

Has variety of services 3.89 3.93 3.90 3.88 3.89 3.60 3.77

Price: 3.91 3.96 3.71 3.96 3.75 3.60 3.67

Adaptable 3.87 3.98 3.61 3.89 3.83 3.70 3.75

Provides quick, efficient

services

3.95 3.94 3.81 4.03 3.68 3.60 3.58

Place: 4.05 4.07 3.98 3.50 3.71 3.90 4.00

My bank is easily

reachable

4.19 4.20 4.07 3.40 3.48 4.20 4.21

I can bank with this bank

whenever I want

3.91 3.94 3.89 3.59 3.93 3.70 3.79

Promotion: 3.48 3.44 3.27 3.54 3.36 3.30 3.26

Offers fun promotions 3.45 3.33 3.20 3.68 3.40 3.10 3.09


merchandise with my bank

name on it

3.51 3.55 3.34 3.40 3.32 3.40 3.43

People: 3.72 3.76 3.68 3.86 3.62 3.60 3.40

Has friendly & courteous

staff

3.85 3.72 3.66 3.85 3.51 3.70 3.42

Is understanding/knowing

the customer

3.50 3.62 3.50 3.46 3.09 3.60 3.50

Staff Response 3.82 3.85 3.64 4.01 3.55 3.80 3.43

Staff Knowledge

Experience and Expertise

3.86 3.77 3.78 3.85 3.96 3.70 3.45




e263

Dressing of Staff Members 3.55 3.85 3.83 4.13 3.99 3.30 3.19

Physical Evidence: 3.78 3.71 3.67 3.85 3.78 3.60 3.53

Has a stylish and attractive

looks

3.74 3.60 3.60 4.09 3.59 3.50 3.31

Physical Ambience

(Cleanliness,Air-

Coditioning, etc.)

4.15 4.21 4.05 4.31 4.28 3.80 3.77

Branch Floor Space 3.98 3.95 3.93 4.00 4.19 3.60 3.82

Adequate Parking Facilities 3.53 3.37 3.18 3.00 2.56 3.40 3.29

Seating/Waiting

Arrangements

3.74 3.48 3.70 3.73 4.07 3.70 3.46

Stationary Availability

(Forms, pins, Gum, etc.)

3.82 3.79 3.74 4.23 4.33 3.70 3.56

I like to visit the website of

my bank

3.50 3.58 3.51 3.61 3.47 3.30 3.51

Process: 3.61 3.61 3.48 3.61 3.53 3.40 3.39

Transparent 3.78 3.69 3.49 4.10 3.91 3.80 3.80

Is convenient to bank with 4.17 4.04 3.82 3.96 3.53 3.80 3.83

Inquiry Counter (Customer

Help Desk)

3.97 3.89 3.71 3.94 3.55 3.60 3.45


Timings

3.59 3.70 3.88 3.65 3.32 3.70 3.57

Combined Mean 3.77 3.78 3.65 3.75 3.67 3.60 3.57

Rank For Banks 2 1 5 3 4 6 7


Table 3.14(B): Group Statistics (Perception for Private Sector Banks on 7ps)

Bank

Brand

Ps of Service

Marketing

Valid N (listwise)

Mean Std.

Deviation

Unweighted Weighted

AXIS PRODUCT 3.832 0.805373 175 175

PRICE 3.91429 1.016288 175 175

PLACE 4.05429 0.888089 175 175

PROMOTION 3.47714 1.021067 175 175

PEOPLE 3.71543 0.826112 175 175

PHYSICAL

EVIDENCE

3.77878 0.763136 175 175

PROCESS 3.87857 0.748493 175 175

HDFC PRODUCT 3.91771 0.668912 175 175

PRICE 3.95714 0.76161 175 175

PLACE 4.07143 0.749932 175 175

PROMOTION 3.44 0.851368 175 175

PEOPLE 3.76114 0.721014 175 175

PHYSICAL 3.71102 0.656435 175 175




e264

EVIDENCE

PROCESS 3.83 0.590903 175 175

ICICI PRODUCT 3.78057 0.799906 175 175

PRICE 3.70857 0.939666 175 175

PLACE 3.98 0.900766 175 175

PROMOTION 3.27143 1.008177 175 175

PEOPLE 3.68229 0.827179 175 175

PHYSICAL

EVIDENCE

3.67429 0.755718 175 175

PROCESS 3.72571 0.784492 175 175

Total PRODUCT 3.84343 0.761339 525 525

PRICE 3.86 0.916827 525 525

PLACE 4.03524 0.848324 525 525

PROMOTION 3.39619 0.965623 525 525

PEOPLE 3.71962 0.792146 525 525

PHYSICAL

EVIDENCE

3.72136 0.72663 525 525

PROCESS 3.81143 0.714432 525 525


Table 3.14(C): Eigenvalues (Perception for Various Banks of Private Sector)


1 .023a 62.5 62.5 0.149

2 .014a 37.5 100 0.116


Table 3.14(E): Standardized Canonical Discriminant Function Coefficients

(Perception for Various Banks of Private Sector)

Service Marketing Ps Function

1 2

PRODUCT -0.481 0.761

PRICE 0.642 0.702

PLACE -0.097 0.102

PROMOTION 0.602 -0.014

PEOPLE -0.74 0.383

PHYSICAL EVIDENCE 0.129 -0.697

PROCESS 0.678 -0.75

Table 3.14(D): Wilks' Lambda (Perception for Various Banks

of Private Sector)

Test of

Function(s)

Wilks'

Lambda

Chi-

square

df Sig.

1 through 2 0.965 18.58 14 0.182

2 0.987 6.984 6 0.322




e265

Table 3.14(F): Structure Matrix (Perception for Various Banks

of Private Sector)

Service Marketing Ps Function

1 2

PRICE .663* 0.564

PROMOTION .601* 0.199

PROCESS .594* 0.081

PHYSICAL EVIDENCE .383* -0.138

PLACE .258* 0.227

PRODUCT 0.23 .567*

PEOPLE 0.139 .302*

Pooled within-groups correlations between discriminating variables

and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any

discriminant function

Table 3.14(G): Unstandardized Canonical Discriminant Function Coefficients


Function

1 2

PRODUCT -0.632 1.001

PRICE 0.704 0.77

PLACE -0.114 0.12

PROMOTION 0.625 -0.015

PEOPLE -0.933 0.483

PHYSICAL EVIDENCE 0.177 -0.959

PROCESS 0.95 -1.051

(Constant) -2.759 -1.475

Table 3.14(H): Functions at Group Centroids


Bank Name Function

1 2

AXIS 0.172 -0.096

HDFC 0.022 0.163

ICICI -0.193 -0.067





e266

Chart 3.7(A): Canonical Discriminant Plot of Private Sector Bank Brands

Chart 3.7(B): Attribute based Perceptual Map of Private Sector Bank Brands




e267

Analysis and Interpretation:

1. Table 3.14(B), shows the mean score for the main 7 Ps of service

marketing for the respective bank. We can see a moderate amount of variation

in the mean figures.

2. Using Table 3.14(E), we can see the significance of respective variable, on

two different functions, which we got, form the Discriminant analysis.

3. Using the standardized coefficients of the attributes on the function 1 and 2

(From Table 3.14(E)) that plot for the different attributes has been plotted as

shown in Figure 2. Based on the distances of the attribute vectors from the

axis, it can be concluded that dimension 1 is heavily related to Promotion and

Process the same can be seen in the Table 3.14(E) with the scores of 0.602 and

0.678, for Promotion and Process respectively. Whereas the Dimension 2 is

heavily related to Product and Price, which is also evident from Table 3.14(E),

with values of 0.761 and 0.702 respectively.

4. Now let us look further and find out the perceptual positioning of banks in

to the minds of customers using the above mentioned attributes.

5. It appears that HDFC is having its unique position on the plot (Chart

3.7(B)) with , high scores on both the dimensions , so we can say HDFC is

strong on both the dimensions equally, HDFC bank is having strong position

on its Product, Price, Promotion and Process. This is also evident from Table

3.14(A), last column where HDFC secured 1st Rank in combined mean of 7Ps.

6. It appears that Axis is having its strong position on the dimension 2

(Process and Promotion).

7. It is also evident from the plot that ICICI is having a strong position on the

dimension 1 (Product and Price).




e268

3.2.3.12: RO_12: To find out customer’s perception for various

banks.

Table 3.15(A): Group Statistics (Perception of Various Banks)

Bank Name Valid N (listwise)

Mean Std. Deviation Unweighted Weighted

AXIS PRODUCT 3.832 0.805373 175 175

PRICE 3.91429 1.016288 175 175

PLACE 4.05429 0.888089 175 175

PROMOTION 3.47714 1.021067 175 175

PEOPLE 3.71543 0.826112 175 175

PHYSICAL EVIDENCE 3.77878 0.763136 175 175

PROCESS 3.87857 0.748493 175 175

HDFC PRODUCT 3.91771 0.668912 175 175

PRICE 3.95714 0.76161 175 175

PLACE 4.07143 0.749932 175 175

PROMOTION 3.44 0.851368 175 175

PEOPLE 3.76114 0.721014 175 175


PROCESS 3.83 0.590903 175 175

ICICI PRODUCT 3.78057 0.799906 175 175

PRICE 3.70857 0.939666 175 175

PLACE 3.98 0.900766 175 175

PROMOTION 3.27143 1.008177 175 175

PEOPLE 3.68229 0.827179 175 175


PROCESS 3.72571 0.784492 175 175

CITI PRODUCT 3.946 0.673213 100 100

PRICE 3.96 0.716614 100 100

PLACE 3.495 1.135948 100 100

PROMOTION 3.54 0.981341 100 100

PEOPLE 3.86 0.792643 100 100


PROCESS 3.9125 0.444346 100 100

HSBC PRODUCT 3.90933 0.614503 75 75

PRICE 3.75333 1.014667 75 75

PLACE 3.70667 0.866389 75 75

PROMOTION 3.36 0.799324 75 75

PEOPLE 3.61867 0.862538 75 75


PROCESS 3.57667 0.933583 75 75

BOB PRODUCT 3.70286 0.773069 175 175

PRICE 3.62571 0.878329 175 175

PLACE 3.92571 0.758228 175 175

PROMOTION 3.29429 0.851281 175 175

PEOPLE 3.61486 0.768643 175 175





e269

PROCESS 3.73429 0.683947 175 175

SBI PRODUCT 3.75657 0.792457 175 175

PRICE 3.66857 0.932421 175 175

PLACE 4 0.925377 175 175

PROMOTION 3.25714 1.023934 175 175

PEOPLE 3.39771 0.854465 175 175


PROCESS 3.66286 0.782288 175 175

Total PRODUCT 3.82 0.753372 1050 1050

PRICE 3.79095 0.908525 1050 1050

PLACE 3.93619 0.894174 1050 1050

PROMOTION 3.36714 0.949746 1050 1050

PEOPLE 3.65467 0.813531 1050 1050


PROCESS 3.76667 0.722334 1050 1050


Table 3.15(B): Correlation Pooled Within-Groups Matrices (Perception of Various Banks)

Correlation PRODUCT PRICE PLACE PROMOTION PEOPLE PHYSICAL

EVIDENCE

PROCESS

PRODUCT 1 0.594 0.537 0.517 0.585 0.578 0.596

PRICE 1 0.512 0.422 0.598 0.53 0.66

PLACE 1 0.378 0.468 0.519 0.517

PROMOTION 1 0.505 0.507 0.459

PEOPLE 1 0.63 0.704

PHYSICAL

EVIDENCE

1 0.64

PROCESS 1

Table 3.15(C): Eigenvalues (Perception of Various Banks)


1 .109a 62.2 62.2 0.313

2 .026a 15 77.2 0.16

3 .018a 10.4 87.6 0.134

4 .016a 8.9 96.5 0.124

5 .005a 3.1 99.6 0.073

6 .001a 0.4 100 0.026


Table 3.15(D): Wilks' Lambda (Perception of Various Banks)


1 through 6 0.845 175.89 42 0

2 through 6 0.937 68.225 30 0

3 through 6 0.961 41.241 20 0.003

4 through 6 0.979 22.458 12 0.033

5 through 6 0.994 6.364 6 0.384

6 0.999 0.728 2 0.695




e270

Table 3.15(E): Standardized Canonical Discriminant Function Coefficients (Perception of Various Banks)

Function

1 2 3 4 5 6

PRODUCT 0.292 -0.786 0.18 0.047 -0.767 -0.308

PRICE 0.306 0.000 0.746 0.719 -0.117 -0.056

PLACE -1.156 0.045 -0.144 0.481 0.159 0.039

PROMOTION 0.040 0.215 0.422 -0.112 0.178 1.113

PEOPLE 0.320 0.435 -1.257 0.507 -0.475 0.26

PHYSICAL EVIDENCE 0.519 -0.408 -0.117 0.161 1.236 -0.386

PROCESS -0.198 1.024 0.513 -0.91 -0.056 -0.567

Table 3.15(F): Structure Matrix (Perception of Various Banks)

Function

1 2 3 4 5 6

PROCESS 0.157 .723* 0.271 0.25 0.031 -0.319

PRICE 0.240 0.367 0.482 .734* -0.081 -0.171

PLACE -0.51 0.226 0.11 .683* 0.145 -0.105

PEOPLE 0.34 0.569 -0.273 .594* -0.09 -0.014

PRODUCT 0.243 -0.021 0.266 .522* -0.255 -0.153

PHYSICAL

EVIDENCE 0.346 0.199 0.058 0.499 .569* -0.207

PROMOTION 0.217 0.309 0.316 0.318 0.154 .621*

Table 3.15(G): Unstandardized Canonical Discriminant Function Coefficients

(Perception of Various Banks) Function

1 2 3 4 5 6

PRODUCT 0.388 -1.046 0.239 0.062 -1.021 -0.41

PRICE 0.34 0 0.827 0.798 -0.13 -0.062

PLACE -1.313 0.051 -0.163 0.547 0.181 0.044

PROMOTION 0.042 0.227 0.445 -0.118 0.188 1.175

PEOPLE 0.398 0.54 -1.563 0.631 -0.59 0.324

PHYSICAL EVIDENCE 0.71 -0.558 -0.159 0.22 1.69 -0.527

PROCESS -0.275 1.427 0.715 -1.268 -0.077 -0.789

(Constant) -0.778 -2.267 -1.302 -3.356 -0.729 1.403




e271

Table 3.15(H): Functions at Group Centroids

Bank Name Function

1 2 3 4 5 6

AXIS -0.043 0.158 0.105 0.068 0.129 0.003

HDFC -0.036 0.054 0.046 0.197 -0.105 0.012

ICICI -0.09 -0.016 -0.198 0.034 0.011 -0.043

CITI 0.854 0.110 0.076 -0.137 -0.045 -0.016

HSBC 0.431 -0.453 -0.071 0.091 0.074 0.033

BOB -0.168 0.093 -0.141 -0.142 -0.015 0.037

SBI -0.336 -0.158 0.176 -0.117 -0.025 -0.014

standardized canonical discriminant functions evaluated at group means

Chart 3.8(A): Canonical Discriminant Plot of Seven Bank Brands




e272


1. Table 3.15(A), shows the mean score for the main 7 Ps of service

marketing for the respective bank. We can see a moderate amount of

variation in the mean figures.

2. Using Table 3.15(E), we can see the significance of respective variable,

on two different functions, which we got, form the Discriminant

analysis.

3. Using the standardized coefficients of the attributes on the function 1

and 2 (From Table 3.15(E)) that plot for the different attributes has

been plotted as shown in x(B). Based on the distances of the attribute

vectors from the axis, it can be concluded that dimension 1 is heavily

Chart 3.8(B): Attribute based Perceptual Map of Seven Bank Brands




e273

related to Promotion and Process the same can be seen in the Table

3.15(E) with the scores of 0.519, 0.306, for Physical Evidence and

Price respectively. Whereas the Dimension 2 is heavily related to

Process and People, which is also evident from Table 3.15(E), with

values of 1.024, 0.435 respectively.

4. Now let us look further and find out the perceptual positioning of

banks in to the minds of customers using the above mentioned

attributes.

5. It appears that HDFC, AXIS and BOB are found to be strong on the

Dimension-1 (Physical Evidence and Price) and Dimension -2 (Process

and People).

6. CITI Bank Scores high on Dimension-1 (Physical Evidence and Price)

because of its premier look branches and scores moderate on

Dimesnion-2 (Process and People).

7. HSBC Banks Scores high (Chart 3.8(B)) on Dimension- 1 (Physical

Evidence and Price) but its score negative on the Dimension-2 (Process

and People). That means customers are not satisfied with the staff of

the bank and its existing process.

8. It is also evident from the plot that ICICI is having a strong position on

the dimension 1 (Product and Price).

3.2.3.13: RO_13: TO find out the Brand personality of various

bank brands in the minds of customers.

A. David Aakar’s Brand Personality Measures:

Now we will follow, Aaker‟s (1997) (refer to chapter 1) Brand Personality

Dimensions and sub Dimensions to find out the brand personality of various

bank brands.

I. Reliability of a Scale:

Mainly, Reliability is a measure of how a scale can be relied on to

produce similar measurements every time we use the scale. SPSS offers




e274

the usual measure of reliability like, Cronbach‟s alpha. If the alpha value

for the scale is 0.70 or more, it is usually considered a good scale.

Looking at Table 3.16(A), we can find the alpha value of 0.910, which

indicates high reliability of the scale. If the item to total correlation

(Table 3.16(B), column 4) is low for an item, we can consider dropping

the item from the scale. Looking at the same table we can find the item to

total correlation is more than 3.0 for all the items which again indicates

high reliability of scale, so we will continue with all the items in our

further research process.

Table 3.16(A): Reliability Statistics (Brand Personality Measures)

Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items

0.91 0.914 27

Table 3.16(B): Item-Total Statistics (Brand Personality Measures)

Brand Personality

Statements

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if

Item

Deleted

Compared to other

banks in this industry,

how well does your

bank satisfy your basic

needs

97.21 260.319 0.496 0.297 0.907

My Bank Gives me

feeling of…Self-

Respect

97.27 254.618 0.561 0.471 0.906

My Bank Gives me

feeling of…Fun 97.95 257.252 0.299 0.322 0.914

My Bank Gives me

feeling of…Excitement 97.76 255.366 0.523 0.417 0.907

My Bank Gives me

feeling of…Innovative 97.16 255.927 0.605 0.467 0.905

Physical Ambience

(Cleanliness, Fresh Air,

Air-Conditioning, Etc.)

at Branch

97.01 260.223 0.513 0.385 0.907

My Bank Brand

is…Memorable 97.05 258.287 0.516 0.461 0.907

My Bank Brand 97.26 256.093 0.555 0.442 0.906




e275

is…Adaptable

My Bank Is convenient

to bank with 97.15 257.946 0.538 0.376 0.906

My bank is easily

reachable 97.01 257.958 0.476 0.353 0.907


Timings 97.39 263.047 0.365 0.226 0.909

I can bank with this

bank whenever I want 97.23 259.909 0.487 0.389 0.907


merchandise with my

bank name on it

97.62 256.837 0.498 0.408 0.907

Compared to other

people, I follow news

about my bank very

closely

97.57 257.361 0.473 0.343 0.908

My Bank Gives me

feeling of…Warmth 97.49 258.252 0.508 0.37 0.907

My Bank Gives me

feeling of…Security 96.97 263.045 0.369 0.361 0.909

My Bank Gives me

feeling of…Social

Approval

97.31 259.336 0.467 0.431 0.908

My Bank Brand

is…Trustworthy 96.92 259.789 0.515 0.391 0.907

My Bank Brand

is…Admirable 97.29 256.864 0.547 0.37 0.906

My Bank Brand

is…Transparent 97.3 257.363 0.501 0.399 0.907

My Bank Is for whole

family 97.44 252.811 0.567 0.402 0.906

My Bank Brand

is…Knowledgeable 97.12 254.152 0.633 0.497 0.905

My Bank Brand

is…Likeable 97.16 258.385 0.51 0.452 0.907

Seating/Waiting

Arrangements at

Branch

97.39 259.047 0.439 0.316 0.908

I really love my bank 97.44 255.729 0.581 0.449 0.906

I am proud to have

others know I bank

with this bank

97.56 252.699 0.597 0.487 0.905

Has high quality

services 97.43 256.73 0.522 0.326 0.907





e276

II. Factor Analysis:

Table 3.16(C): Statement, Loadings and Communalities (Brand Personality Measures)

Brand Personality Factors Initial Commu

nalities

Loadin

gs

Rotated

Loadings

F1: Competence

Compared to other banks in this industry, how well does

your bank satisfy your basic needs

1 0.458 0.544 -0.037

Self-Respect 1 0.633 0.611 0.333

F2: Excitement

Fun 1 0.632 0.338 0.107

Excitement 1 0.625 0.553 0.125

Innovative 1 0.498 0.651 0.329

Physical Ambience (Cleanliness, Fresh Air, Air-

Conditioning, Etc.)

1 0.624 0.563 0.065

F3: Ruggedness

Memorable 1 0.687 0.575 0.167

Adaptable 1 0.56 0.609 0.562

Is convenient to bank with 1 0.443 0.587 0.222

My bank is easily reachable 1 0.444 0.53 0.253

Banking Hours/Branch Timings 1 0.419 0.413 0.08

I can bank with this bank whenever I want 1 0.529 0.538 0.516

I would be interested in merchandise with my bank

name on it

1 0.579 0.549 0.152

Compared to other people, I follow news about my bank

very closely

1 0.591 0.521 0.033

F4: Sincerity

Warmth 1 0.556 0.542 0.077

Security 1 0.676 0.419 0.057

Social Approval 1 0.658 0.51 0.082

Trustworthy 1 0.506 0.569 0.429

Admirable 1 0.455 0.602 0.305

Transparent 1 0.621 0.554 0.728

Is for whole family 1 0.543 0.621 0.612

F5: Sophistication

Knowledgeable 1 0.53 0.687 0.480

Likeable 1 0.675 0.565 0.192

Seating/Waiting Arrangements 1 0.605 0.491 0.166

I really love my bank 1 0.551 0.63 0.519

I am proud to have others know I bank with this bank 1 0.62 0.644 0.214

Has high quality services 1 0.379 0.57 0.326

Extraction Method: Principal Component Analysis, Total Variance: 55.91%,

KMO: 0.921, Chi-Square:9877.282, df: 351, Sig.: 0.000


Interpretation:

1. In determining, the relative power of each factor variable, factor




e277

loadings are considered to be more valid and in general any variable

with a loading of 0.30 or higher is considered to be statistically

significant (Hair et al., 1998).

2. Looking at Table 3.16(C), we can find the Communalities, Factor

Loadings and Rotated Factor Loadings for each item. We can see that

the loadings for each variable are high and above 0.30 values which

indicate good representative of customer‟s choice. Well, if we

consider to reduce some items, from the scale than we can use

Rotated Factor Loadings (Table 3.16(C), Column 5), the items written

in Bold letter represent high association with values more than 0.30.

3. We will use only these highlighted items with higher loadings, in our

further study of attribute mapping for various banks.

III. Attribute Based Perceptual Mapping:

Now we will use the average score of the selected items (Table 3.16(C),

Column 5) and will try to draw an attribute based perceptual map of all

the banks on two dimensions to find out the position of a bank brand in to

customers mind.

Table 3.16(D): Group Statistics (Brand Personality Measures)


Unweighted Weighted

AXIS F1_COMPETENCE 3.9086 1.18063 175 175

F2_EXCITEMENT 3.88 1.26509 175 175

F3_RUGGEDNESS 3.8943 0.93555 175 175

F4_SINCERITY 3.8743 0.7982 175 175

F5_SOPHISTICATION 3.6629 0.8977 175 175

HDFC F1_COMPETENCE 3.7371 1.15447 175 175

F2_EXCITEMENT 4.0571 0.82151 175 175

F3_RUGGEDNESS 3.96 0.71607 175 175

F4_SINCERITY 3.86 0.75552 175 175


ICICI F1_COMPETENCE 3.6 1.1547 175 175

F2_EXCITEMENT 3.8457 1.10077 175 175

F3_RUGGEDNESS 3.7486 0.89197 175 175

F4_SINCERITY 3.5886 0.91091 175 175




e278


CITI F1_COMPETENCE 4.07 0.83188 100 100

F2_EXCITEMENT 4.02 0.66636 100 100

F3_RUGGEDNESS 3.74 0.93062 100 100

F4_SINCERITY 3.9575 0.4021 100 100


HSBC F1_COMPETENCE 3.8267 1.40821 75 75

F2_EXCITEMENT 4.5067 0.74204 75 75

F3_RUGGEDNESS 3.88 0.76158 75 75

F4_SINCERITY 3.8533 0.819 75 75


BOB F1_COMPETENCE 3.7657 1.16805 175 175

F2_EXCITEMENT 3.6571 0.92048 175 175

F3_RUGGEDNESS 3.6686 0.82098 175 175

F4_SINCERITY 3.8786 0.81467 175 175


SBI F1_COMPETENCE 3.7543 1.1949 175 175

F2_EXCITEMENT 3.7086 1.10936 175 175

F3_RUGGEDNESS 3.7743 0.89966 175 175

F4_SINCERITY 3.8071 0.81932 175 175


Total F1_COMPETENCE 3.7886 1.16555 1050 1050

F2_EXCITEMENT 3.8962 1.02573 1050 1050

F3_RUGGEDNESS 3.8076 0.86072 1050 1050

F4_SINCERITY 3.8202 0.79636 1050 1050


Table 3.16(E): Tests of Equality of Group Means (Brand Personality Measures)

Wilks' Lambda F df1 df2 Sig.

F1_COMPETENCE 0.988 2.165 6 1043 0.044

F2_EXCITEMENT 0.954 8.365 6 1043 0

F3_RUGGEDNESS 0.987 2.362 6 1043 0.029

F4_SINCERITY 0.981 3.403 6 1043 0.002

F5_SOPHISTICATION 0.981 3.384 6 1043 0.003




e279

Table 3.16(F): Correlation: Pooled Within-Groups Matrices (Brand Personality

Measures)

Personality

Factors

F1_COMPE

TENCE

F2_EXCIT

EMENT

F3_RUGGE

DNESS

F4_SINC

ERITY

F5_SOPHIS

TICATION

F1_COMPET

ENCE 1 0.376 0.342 0.496 0.485

F2_EXCITEM

ENT 0.376 1 0.38 0.506 0.571

F3_RUGGED

NESS 0.342 0.38 1 0.59 0.589

F4_SINCERI

TY 0.496 0.506 0.59 1 0.67

F5_SOPHISTI

CATION 0.485 0.571 0.589 0.67 1

Table 3.16(G): Eigenvalues (Brand Personality Measures)


1 .075a 51.1 51.1 0.263

2 .038a 26 77.1 0.191

3 .024a 16.4 93.4 0.153

4 .006a 3.8 97.2 0.074

5 .004a 2.8 100 0.063


Table 3.16(H): Wilks' Lambda (Brand Personality Measures)

Test of Function(s) Wilks' Lambda Chi-square Df Sig.

1 through 5 0.867 148.278 30 0

2 through 5 0.932 73.312 20 0

3 through 5 0.967 34.56 12 0.001

4 through 5 0.99 9.975 6 0.126

5 0.996 4.198 2 0.123

Table 3.16(I): Standardized Canonical Discriminant Function Coefficients

(Brand Personality Measures)

Brand Personality Factors Function

1 2 3 4 5

F1_COMPETENCE -0.18 -0.139 0.313 -0.883 0.698

F2_EXCITEMENT 1.067 0.055 0.464 -0.219 -0.392

F3_RUGGEDNESS 0.522 -0.832 -0.326 0.433 0.676

F4_SINCERITY -0.786 -0.337 0.923 0.53 -0.605

F5_SOPHISTICATION -0.21 1.336 -0.456 0.398 0.435




e280

Table 3.16(J): Structure Matrix (Brand Personality Measures)

Function

1 2 3 4 5

F2_EXCITEMENT .681* 0.279 0.665 0.108 0.069

F5_SOPHISTICATION 0.094 .584* 0.388 0.455 0.542

F4_SINCERITY -0.167 0.026 .815* 0.503 0.233

F1_COMPETENCE -0.091 0.077 0.613 -0.362 .692*

F3_RUGGEDNESS 0.279 -0.27 0.234 0.594 .665*

Pooled within-groups correlations between discriminating variables and

standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant

function

Table 3.16(K): Unstandardized Canonical Discriminant Function Coefficients (Brand

Personality Measures)

Function

1 2 3 4 5

F1_COMPETENCE -0.155 -0.12 0.27 -0.76 0.6

F2_EXCITEMENT 1.062 0.055 0.462 -0.218 -0.39

F3_RUGGEDNESS 0.608 -0.97 -0.381 0.505 0.789

F4_SINCERITY -0.993 -0.426 1.167 0.669 -0.765

F5_SOPHISTICATION -0.256 1.631 -0.556 0.486 0.531

(Constant) -1.121 -0.515 -3.757 -2.559 -2.813

Table 3.16(L): Functions at Group Centroids (Brand Personality Measures)

Bank Name Function

1 2 3 4 5

AXIS -0.021 -0.224 0.09 -0.038 0.072

HDFC 0.214 -0.031 0.008 0.143 0.035

ICICI 0.181 0.102 -0.297 -0.053 0.013

CITI -0.149 0.357 0.19 -0.071 0.085

HSBC 0.626 0.124 0.242 -0.05 -0.125

BOB -0.4 0.147 -0.012 0.052 -0.059

SBI -0.157 -0.251 -0.002 -0.043 -0.056





e281


1. Table 3.16(D) shows the mean of all type of personality dimensions for

the various banks.

2. Table 3.16(E) (Test of Equality of group means), we can find that all

the variables are having their significance value less than 0.10 , which

indicates all the variable are highly affected on the Bank Brand Name at

90% confidence level, The same can be concluded looking at Lambda

Value of >+ 0.70.

3. Table 3.16(F) (Correlation), we can see that all the variables are more

or less related with each other moderately.

4. Using Table 3.16(G). We can find the Eigen value as well significance

level of all the variables, which is found to be near to 0.000 which is a

good indicator.

5. Table 3.16(I), represent standardized canonical discriminant functions,

where we can find Excitement (1.067) and Ruggedness (0.522)

represents Dimension 1, and Sophistication (1.336) represents

Dimension 2.

6. Table 3.16(L), represents functions at group centroid.

7. Using the standardized coefficients of the attributes on the function 1

and 2 (From Table 3.16(I)) the plot for the different attributes has been

plotted as shown in Chart 3.9(A). Based on the distances of the attribute

vectors from the axis, it can be concluded that dimension 1 is heavily

related to Excitement and Ruggedness; the same can be seen in the

Table 3.16(I), Where as the Dimension 2 is heavily related to

Sophistication, which is also evident from Table 3.16(I).

8. Now let us look further and find out the perceptual positioning of banks

in to the minds of customers using these attributes (Combining Table

3.16(I) and Table 3.16(L)).

9. It is evident form Chart 3.9(B), CITI is having its unique position on

the plot with high score on dimension 2 (Sophistication).

10. Now you can find that HSBC followed by HDFC and ICICI are having

their unique position on dimension 1 (Excitement and Ruggedness).




e282

11. Whereas we can find that banks BOB, SBI and AXIS are not able to

create their distinctive image on any dimensions.

Chart 3.9(B): Perceptual Mapping of Brand Personality of Bank Brands

Chart 3.9(A): Canonical Discriminant Functions of Brand Personality

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

COMPETENCE

EXCITEMENT

RUGGEDNESS

SINCERITY

SOPHISTICATION

AXIS

HDFC

ICICI

CITI

HSBC

BOB

SBI




e283

In order to conclude we can say that all five personality factors of Aaker

(1991) are having some unique perceptions for defining the brand as a person.

Here we have found that as the HDFC and ICICI bank is easily reachable &

offers good promotions, they have been personified with the characteristics

like Excitement and Ruggedness, a bank for all age and income group right.

Whereas the CITI bank is a bank for HNI customer, a premium customer

segment bank, which is rightly personified with characteristic like

Sophistication, This means an upper class (Premier) bank.

3.2.3.14: RO_14: To find out the potential sources for improving

the existing brand image of banks.

Table 3.16(M):Steps for improving the existing brand image of the bank as per managers

view

Sr.

No.

If you have been promoted as the CEO, what is the first step you

will take to improve the Brand Image of this bank?

Bank Name

A

X

I

S

H

D

F

C

I

C

I

C

I

C

I

T

I

H

S

B

C

B

O

B

S

B

I

C C C C C C C

1 No Response (Blanks) 3 1 5 0 1 0 1

2 I will Change the Entire Marketing Department 0 2 0 0 0 0 0

3 I will stop selling insurance products 1 0 0 0 0 0 0

4 Increase the level of trust by introducing the value of our Brand to

all our banking customers

0 0 0 1 0 0 0

5 Introduce More Products 0 0 0 0 1 0 0

6 Make the system more user friendly 0 0 0 2 0 0 0

7 Quick Customer Services 0 2 0 0 0 0 3

8 Regular updating of product knowledge to staff members 0 0 0 0 0 0 1

9 Rewards to Bank Employees and Customers 1 0 0 0 0 0 0

10 Specialized BPRs 0 0 0 0 0 0 1

11 Start 8 To 8 Banking in All Branches in India and Overseas 0 0 0 0 0 3 0

12 Strengthen the economic ties with people by giving them more

opportunities to work in it.

0 0 1 0 0 0 0

13 To Focus on Expanding our Customer Base 1 0 0 0 0 0 0

14 Up till now whatever strategies defined by our management is

designed well in advance to cop up with market situation and bank's

vision/mission and corporate objective. So if I would have been

promoted as CEO then I would definitely ensure that bank doesn't

move from its game plan.

0 1 0 0 0 0 0




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15 VRS Scheme for all the employees > 50 Years of Age, Recruiting

Young Generation Staff, & Recruiting Local IT Staff for internal IT

and Hardware Problems at Branch Level

0 0 0 0 0 2 0

16 Will try to change the current image of 'Deposit Taking Bank' to

'Credit Providing Bank'

0 0 0 0 0 1 0


Looking at the above table we can see that lot of different options are given by

the existing branch managers to improve the existing brand image of their bank.

Some very good suggestions were received like 8 to 8 banking for all branches,

VRS schemes should be allowed to all employee of >50 years in PSU banks,

etc.

3.3: Hypotheses Testing

It is an important part of a marketing research to test the pre-specified hypothesis

and derive the conclusions regarding the marketing research problem. Here we

will test the pre-specified 54 hypothesis in congruence with the research questions.

3.3.1: RQ_01: Does ‘Brand Loyal’ customers recommend the

Bank Brand to others?

Null Hypothesis:

H0_01: Customer‟s loyalty and customer‟s decision to recommend the bank to

others are independent with each other.

Alternate Hypothesis:

H1_01: Customer loyalty and customer‟s decision to recommend the bank to

others are dependent on each other.

Table 3.17(A): Brand Loyalty Level and Recommendation Cross Tabulation

I consider myself to be a loyal customer of this bank

Total I will recommend

this bank to others

Strongly

Disloyal Disloyal

Neither

Loyal nor

Disloyal

Loyal Strongly

Loyal

Strongly Not

Recommend

8 3 6 8 7 32

25.00% 9.40% 18.80% 25.00% 21.90% 100.00%




e285

Might Not

Recommend

5 23 17 12 8 65

7.70% 35.40% 26.20% 18.50% 12.30% 100.00%

Might Recommend or

Might Not

Recommend

12 18 75 54 47 206

5.80% 8.70% 36.40% 26.20% 22.80% 100.00%

Might Recommend 7 11 41 121 132 312

2.20% 3.50% 13.10% 38.80% 42.30% 100.00%

Strongly Recommend 7 7 27 85 309 435

1.60% 1.60% 6.20% 19.50% 71.00% 100.00%

Total 39 62 166 280 503 1050

3.70% 5.90% 15.80% 26.70% 47.90% 100.00%


Chart 3.10: Brand Loyalty Level and Recommendation Cross Tabulation

Table 3.17(B): Chi-Square Test (Bank Loyalty Level and Bank Recommendation Cross

tabulation)

Value Df Asymp. Sig. (2-sided)

Pearson Chi-Square 380.179a 16 0

Likelihood Ratio 312.448 16 0

Linear-by-Linear Association 230.623 1 0

N of Valid Cases 1050




e286

Table 3.17(C): Symmetric Measures (Bank Loyalty Level and Bank Recommendation

Cross tabulation)

Measures Value Asymp. Std.

Errora

Approx.

Tb

Approx.

Sig.

Lambda

Symmetric 0.128 0.022 5.632 0

I will recommend this bank to

others Dependent 0.171 0.028 5.641 0

I consider myself to be a loyal

customer of this bank Dependent 0.08 0.023 3.413 0.001

Phi 0.602 0

Contingency Coefficient 0.516 0


1. Looking at Table 3.17(A), we can observe that when the customer feels

that they are strongly loyal to a Bank Brand, then 71 percent of the

customers will Strongly Recommend the Bank Brand to others in their

group, the same can be observed wit disloyal customers that 35 percent of

the disloyal customers might not recommend a Bank Brand to other in their

group. So we can conclude that Bank Brand Recommendation decision

does affect by the loyalty of the existing customer with a Bank Brand, the

same can be revealed by looking at Chart 3.10.

2. Looking at Table 3.17(B), the significance value of 0.000, we can say that

the variables like Bank Brand Loyalty and Bank Recommendation

Decision is dependent on each other.

3. Looking at Table 3.17(C), the Lambda value of 0.171, we can say that 17

percent of the times we can predict the customer‟s recommendation

decision when the Brand Loyalty Level of the customer is know.

4. Again looking at Table 3.17(C), where the Phi value can range between –1

to +1 depending on the negative association to positive association between

two variables, here the Phi value is „0.620‟, Contingency Coefficient

(0.516) values are near to „1‟, which shows a strong positive association

between the means of Brand Loyalty and Customer‟s Readiness to

recommend the bank brand to others.




e287

5. Using the Table 3.17(B), and Table 3.17(C), we reject the null hypothesis

H0_01, and accept the alternate hypothesis H1_01: Customer loyalty and

customer‟s decision to recommend the bank to others are dependent on

each other.

3.3.2: RQ_02: Does the ‘Brand Awareness’ have any effect on

customer future purchase decision with the same brand?

Null Hypothesis:

H0_02: „Brand Awareness‟ of bank does not lead to consumer‟s readiness to

use future products/services of the bank.


H1_02: „Brand Awareness‟ of bank does leads to consumer‟s readiness to use

future products/services of the bank.

We will use the variable „Brand Awareness‟, which is the Mean of the answers

given to the following statements by Bank customers:

I will recommend this bank to others

My bank‟s brand is Memorable

I really identify with people who bank with this bank

Identify the corporate color of your bank brand

We will now recode the numeric value of the mean in to nominal scale:

Exhibit 3.5: Recoding of Brand Awareness Score

Value Label

0 to 1 Poor

1 to 2 Low

2 to 3 Medium

3 to 4 High

Table 3.18(A): Future Product Purchase Intention and BRAND AWARNESS Cross

tabulation

Purchase

Intention for

Future Products

BRAND AWARENESS LEVEL

Total HIGH MEDUIM LOW POOR

Strongly will not

Purchase

19 30 8 2 59

3.00% 7.80% 25.80% 33.30% 5.60%




e288

Might not Purchase 24 45 5 0 74

3.80% 11.70% 16.10% 0.00% 7.00%

Might Purchase or

Might Not

Purchase

108 104 11 4 227

17.20% 27.00% 35.50% 66.70% 21.60%

Might Purchase 189 128 5 0 322

30.10% 33.20% 16.10% 0.00% 30.70%

Strongly Purchase 288 78 2 0 368

45.90% 20.30% 6.50% 0.00% 35.00%

Total 628 385 31 6 1050

100.00% 100.00% 100.00% 100.00% 100.00%


Chart 3.11: Purchase Intention for Future Product and BRAND AWARENESS Cross tabulation

Table 3.18(B): Chi-Square Tests (Future Purchase Intention and BRAND

AWARNESS)

Value df Asymp. Sig. (2-sided)





a. 28 cells (40.0%) have expected count less than 5. The minimum expected count is

.11.




e289

Table 3.18(C): Directional Measure (Future Purchase Intention and BRAND

AWARNESS)

Valu

e

Asymp. Std.

Errora

Approx.

Tb

Approx.

Sig.

Symmetric 0.05

6

0.018 3.009 0.003

I will Purchase other

products/services offered by this

bank in near future Dependent

0.10 0.026 3.616 0

BRAND AWARENESS

Dependent

0.02

2

0.019 1.172 0.241

Table 3.18(D): Symmetric Measures (Future Purchase Intention and BRAND

AWARNESS)

Measures Value Approx. Sig.

Phi .529 .000

Contingency Coefficient .468 .000

N valid cases 1050


1. Looking at Table 3.18 (A), we can find that , when Brand Awareness is

HIGH, 45% of the customers have replied that they will Strongly Purchase, the

product/services offer by their Bank Brand in near future. But when customers

feel that Brand Awareness is Poor than 33% of the customer will Strongly Not

Purchase, the products/services offered by their Bank Brand in near future.

2. Looking at Table 3.18(B), we can find the Significance of 0.000, which

indicates Bank Brand Awareness and Customers Readiness to Purchase Future

Products/Services are correlated with each other.

3. Looking at Table 3.18(C), the Lambda value is 0.10 which indicates when

the Customer‟s Brand Awareness Level is know , 10% of the times we can find

his purchase intention for the future products/service by the same bank brand.


to +1 depending on the negative association to positive association between two

variables, here the Phi value is „0.529‟, Contingency Coefficient (0.468) values

near to „1‟, which shows a good positive association between Brand Awareness

and Customer‟s Readiness to purchase products/services in near future.




e290

5. The Contingency Coefficient value of 0.468 indicates higher correlation

between two variables like, Brand Awareness and Intention to purchase future

products/services.

So we reject the null hypothesis and accept the alternate hypothesis, H1_02:

„Brand Awareness‟ of bank does leads to consumer‟s readiness to use future

products/services of the bank.

3.3.3: RQ_03: Is there any impact of quality on the customer’s

loyalty decisions?

Null Hypothesis:

H0_03: Bank‟s service quality and consumer‟s loyalty are not correlated


H1_03: Bank‟s service quality and consumer‟s loyalty are correlated.

Let us calculate, Perceived quality = Mean (Provides Quick and Efficient

Services, and my bank has high quality services)

Table 3.19(A): Perceived Quality and Customer Loyalty cross Tabulation

Loyalty

Level

PERCEIVED QUALITY (Mean) Total

1 1.5 2 2.5 3 3.5 4 4.5 5

Strongly

Disloyal

6 3 5 2 3 11 7 0 2 39

54.5

0%

25.00

%

14.30

% 3.00% 2.10% 5.50% 2.60% 0.00% 1.50% 3.70%

Disloyal

0 4 11 11 14 9 7 4 2 62

0.00

%

33.30

%

31.40

%

16.40

% 9.90% 4.50% 2.60% 2.20% 1.50% 5.90%

Neither

Loyal

nor

Disloyal

0 1 4 15 35 51 27 26 7 166

0.00

%

8.30

%

11.40

%

22.40

%

24.80

%

25.60

%

10.20

%

14.00

% 5.20%

15.80

%

Loyal

3 1 11 14 39 50 88 55 19 280

27.3

0%

8.30

%

31.40

%

20.90

%

27.70

%

25.10

%

33.20

%

29.60

%

14.20

%

26.70

%

Strongly

Loyal

2 3 4 25 50 78 136 101 104 503

18.2

0%

25.00

%

11.40

%

37.30

%

35.50

%

39.20

%

51.30

%

54.30

%

77.60

%

47.90

%

Total 11 12 35 67 141 199 265 186 134 1050

100.

00%

100.0

0%

100.0

0%

100.0

0%

100.0

0%

100.0

0%

100.0

0%

100.0

0%

100.0

0%

100.0

0%





e291

Table 3.19(B): Chi-Square Tests (Perceived Quality and Customer Loyalty)






Table 3.19(C): Symmetric Measures (Perceived Quality and Customer Loyalty)

Value Approx. Sig.

Nominal by Nominal Phi 0.537 0



Chart 3.12: Perceived Quality and Customer Loyalty


1. Looking at Table 3.19(A), we can find that when the Perceived Quality mean

is 5 (High), the Customers feel Strongly Loyal with a bank brand, where as

when the mean is 1(Low), 54% of the customers feel strongly disloyal to the

bank brand.




e292

2. Looking at Table 3.19(B), we can find Chi-Square significance of 0.000,

which indicates a high correlation between perceived quality and customer

loyalty.

3. Again looking at Table 3.19(C), where the Phi value can range between –1 to

+1 depending on the negative association to positive association between two

variables, here the Phi value is „0.537‟, Contingency Coefficient (0.473)

values near to „0‟, which shows a strong difference between the means of

different groups which shows a good correlation between Perceived Quality

and Customer Loyalty.

Looking at Table 3.19(B) and Table 3.19(C), we reject the null hypothesis H0_3

and accept the alternate hypothesis H1_03: Bank‟s service quality and

consumer‟s loyalty are correlated.

3.3.4: RQ_04: Does the bank’s ‘Brand Associations’ have any

effect on the customer’s satisfaction?

Null Hypothesis:

H0_04: Bank‟s „Brand Association‟ does not influence on customer satisfaction.


H1_04: Bank‟s „Brand Association‟ does influence on customer satisfaction.

Brand Association is the mean of the below given statements by the customers:

My bank gives me a feeling of…Warmth

My bank gives me a feeling of… Fun

My bank gives me a feeling of… Excitement

My bank gives me a feeling of… Security

My bank is… Innovative

My bank is… Knowledgeable

My bank is… Trustworthy

My bank is… Likeable

My bank is… Admirable

I really love my bank




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Table 3.20(A): Chi-Square Tests (Brand Association and Customer Satisfaction)






a. 70 cells (58.3%) have expected count less than 5. The minimum expected count is .05.

Table 3.20(B): Directional Measures (Brand Association and Customer Satisfaction)

Value

Asymp.

Std.

Errora

Approx.

Tb

Approx.

Sig.

Lambda

Symmetric 0.054 0.015 3.601 0

Please tike one of the

following statement

about your Bank

Dependent

0.127 0.037 3.238 0.001

BRAND

ASSOCIATIONS

Dependent

0.021 0.009 2.363 0.018

Goodman and

Kruskal tau


following statement

about your Bank

Dependent

0.121 0.015 .000c

BRAND

ASSOCIATIONS

Dependent

0.006 0.001 .000c

Uncertainty

Coefficient

Symmetric 0.058 0.007 8.392 .000d


following statement

about your Bank

Dependent

0.142 0.016 8.392 .000d

BRAND

ASSOCIATIONS

Dependent

0.036 0.004 8.392 .000d




e294

Table 3.20(C): Symetric Measures (Brand Association and Customer Satisfaction)

Value Approx. Sig.

Phi 0.569 0


Valid N 1050


1. Looking at Table 3.20(A), the significance value of 0.000 indicates high

dependability between two variables Brand Associate and Customer Satisfaction.

2. Looking at Table 3.20(B), we can find the lambda value 0.127, which

indicates that there is 12% chances in predicting Customer Satisfaction Level,

when the Brand Association Level of a bank is know. The same is reveled using

Goodman and Kruskal Tau and Uncertainty Coefficient.


to +1 depending on the negative association to positive association between two

variables, here the Phi value is „0.569‟, but phi coefficient is genteraly used for

two by two cross tabulations (Malhotra, 2004) for the higher tables we can use

Contingency Coefficient and Cramer‟s‟ V. Contingency Coefficient (0.494)

values near to „0‟, which shows a good positive association between Brand

Associations and Customer‟s satisfaction.

Looking at chi-square significance of 0.000 (Table 3.20(A)), we reject the null

hypothesis and accept the alternate hypothesis H1_04: Bank‟s „Brand

Association‟ does influence on customer satisfaction.

3.3.5: RQ_05: Does the ‘Relative Advantage’ of the bank brand

helps reducing the effects of competitive moves on customers?

Null Hypothesis:

H0_05: „Relative Advantage‟ perceived in a bank brand does not influence

customer‟s response to competitive moves by competitors.





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H1_05: „Relative Advantage‟ perceived in a bank brand do influence customer‟s

response to competitive moves by competitors.

We will take, Relative Advantage = compared to other banks in this industry

how well your bank satisfies your basic needs (refer to Q.8 in customer‟s

questionnaire: Annexure 1)

Table 3.21(A): Relative Advantage and Competitive moves Cross Tabulation

I will switch to a competitor

bank, that offers more

attractive benefits

RELATIVE ADVANTAGE

Total 1:Poor 2:Low 3:AVG 4:Good 5:High

Strongly Disagree 3 8 17 48 62 138

11.5% 11.8% 7.5% 10.8% 21.9% 13.1%

Disagree 0 12 29 48 24 113

.0% 17.6% 12.7% 10.8% 8.5% 10.8%

Neither Agree nor Disagree 1 13 60 108 52 234

3.8% 19.1% 26.3% 24.3% 18.4% 22.3%

Agree 6 11 65 127 42 251

23.1% 16.2% 28.5% 28.5% 14.8% 23.9%

Strongly Agree 16 24 57 114 103 314

61.5% 35.3% 25.0% 25.6% 36.4% 29.9%

Total 26 68 228 445 283 1050

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%


Chart 3.13: Relative Advantage and Competitive moves




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Table 3.21(B): Chi-Square Tests (Relative Advantage and Competitive moves)




Linear-by-Linear Association 5.415 1 0.02


a. 2 cells (8.0%) have expected count less than 5. The minimum expected count is 2.80.


1. Looking at Table 3.21(A), we can find that, when the Relative Advantage

is High (5), 21% of the customers replied , „Strongly Disagree‟, to the statement

that “they will switch to a competitor bank , that offers more attractive benefits”,

whereas we can see that 61% of the customer who feel that the Relative

Advantage of a Bank Brand is Low(1), have replied „Strongly Agree‟ to the

statement that “they will switch to a competitor bank, that offer more attractive

benefits”

2. Looking at the Ch-Square Significance value of 0.000 in Table 3.21(B), we

can find a strong correlation between variable like, Relative Advantage and

Response to Competitive Moves.

Looking at Table 3.21(A), and the sig. value in Table 3.21(B), at 90%

confidence level, we reject the null hypothesis and accept the alternate

hypothesis H1_05: „Relative Advantage‟ perceived in a bank brand does

influence customer‟s response to competitive moves by competitors.

3.3.6: RQ_06: Does the ‘Compatibility’ of the bank brand

improve customer satisfaction?

Null Hypothesis:

H0_06: The compatibility of a bank brand is not related to satisfaction of the

customer.





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H1_06: The compatibility of a bank brand is related to satisfaction of the

customer.

We will now calculate the compatibility level of a bank brand with mean of the

answers given to following statements by their customers (refer to Customer

questionnaire in Annexure 1):

My bank… Has a stylish and attractive looks (Q12.g)

I am Satisfied with the following services of my bank branch… Staff

Response (Q14.h)

I am always interested in learning more about my bank (Q15.g)

I like to visit the website of my bank (Q15.j)

Compared to other people, I follow news about my bank very closely

(Q15.k)

Now we will convert the mean into nominal scale variable with following logic

(Exhibit 3.6):

Exhibit 3.6: Recoding of Compatibility Mean

Mean Value Variable Label

1 to 2 Poor

2 to 3 Low

3 to 4 Average

4 to 5 High

Table 3.22 (A): Correlations (Satisfaction Level and Compatibility)

Loyalty Level COMPATIBILITY

Loyalty Level Pearson Correlation 1 .323**

Sig. (2-tailed) 0

COMPATIBILITY Pearson Correlation .323** 1

Sig. (2-tailed) 0

**. Correlation is significant at the 0.01 level (2-tailed).




e298

Table 3.22(B): Satisfaction Level and Compatibility Cross Tabulation

Satisfaction Level COMPATIBILITY

Total AVERAGE HIGH LOW POOR

I am totally satisfied & don't

want to switch

173 180 44 2 399

37.50% 46.00% 26.30% 6.50% 38.00%

I am satisfied but expect more

improvements in service

275 204 105 16 600

59.70% 52.20% 62.90% 51.60% 57.10%

I am totally dissatisfied and

want to switch over

13 7 18 13 51

2.80% 1.80% 10.80% 41.90% 4.90%

Total 461 391 167 31 1050

100.00% 100.00% 100.00% 100.00% 100.00%

Table 3.22(C): Chi-Square Tests (Satisfaction Level and Compatibility)






a. 23 cells (38.3%) have expected count less than 5. The minimum expected count is .15.

Table 3.22(D): Symmetric Measures (Satisfaction Level and Compatibility)

Value Approx. Sig.





1. Looking at Table 3.22(A), we can find the Correlation between Loyalty

Level of Customer and Compatibility of a Bank brand is statistically

Significant at 90% confidence level with value of 0.323 that means they are

related with each other at least 32% of the time.

2. In the Table 3.22(B), we can find that when the Compatibility Level is

High 46% of the customer replay that they are totally satisfied and don‟t

want to switch, where as we can find that when the compatibility level is




e299

Poor, 42% of the customers replied that they are totally dissatisfied and

want to switch over.

3. Looking at the chi-square sig. value of 0.000 in Table 3.22(C), we can find

that, there is a statistically significant correlation between Compatibility

Level and Satisfaction Level of a bank brand.

4. Looking at Contingency Coefficient value of 0.438 (Table 3.22(D)) that is

close to 0 we can say that there is a strong correlation between these two

variables i.e., Bank Brand Compatibility and Satisfaction Level of

customer.

5. looking at Table 3.22(D), where the Phi value can range between –1 to +1

depending on the negative association to positive association between two

variables, here the Phi value is „0.488‟, that is near to „0‟, which shows a

good positive association between Brand‟s Compatibility level and

Customer‟s satisfaction. But using phi coefficient is restricted to the table

of two by two only, for larger tables Cramer‟s V and Contingency

coefficients are better (Singh, 2009)2.

At last looking at Table 3.22(C) and Table 3.22(D), we reject the null hypothesis

and accept the alternate hypothesis Alternate Hypothesis H1_06: The

compatibility of a bank brand is related to satisfaction of the customer.

3.3.7: RQ_07: Does the complexity of operating an account

reduce customer’s satisfaction?

Null Hypothesis:

H0_07: Complexity level of operating a bank account and customer satisfaction

with a bank brand is not correlated,


H1_07: Complexity level of operating a bank account and customer satisfaction

with a bank brand is correlated

2 Singh Kultar (2009), Quantitative Social Research Methods, Sage Publications, New Delhi, India.

p.128.




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The Variable Complexity Level will be used by calculating a mean of the score

of following statements by customer:

My bank is… convenient to bank with(Q12.a)

My bank is… Knowing/Understanding the customers(Q12.i)

I am satisfied with the Branch Timings of My Bank Branch(Q14.k)

I can bank with this bank whenever I want (Q15.K)

Table 3.23(A): SATISFACTION LEVEL and COMPLEXITY LEVEL Cross

tabulation

COMPLEXITY LEVEL Total SATISFACTION

LEVEL LESS

COMPLEX SOMEWHAT

COMPLEX COMPLEX

HIGHLY

COMPLEX

I am totally

satisfied & don't

want to switch

234 115 46 4 399

46.60% 34.10% 25.10% 14.30% 38.00%

I am satisfied but

expect more

improvements in

service

261 205 121 13 600

52.00% 60.80% 66.10% 46.40% 57.10%

I am totally

dissatisfied and

want to switch

over

7 17 16 11 51

1.40% 5.00% 8.70% 39.30% 4.90%

Total 502 337 183 28 1050

100.00% 100.00% 100.00% 100.00% 100.00%

Chart 3.14: Complexity Level and Satisfaction Level




e301

Table 3.23(B): Correlations (Complexity Level and Satisfaction Level)

SATISFACTION

LEVEL

COMPLEXITY

LEVEL

SATISFACTION

LEVEL

Pearson

Correlation

1 -.281**

Sig. (2-tailed) 0

N 1050 1050

COMPLEXITY

LEVEL

Pearson

Correlation

-.281** 1

Sig. (2-tailed) 0

N 1050 1050


Table 3.23(C): Chi-Square Tests (Complexity Level and Satisfaction Level)





Table 3.23(D): Symmetric Measuresa (Complexity Level and Satisfaction Level)

Value Approx. Sig.


Cramer's V 0.235 0



a. Correlation statistics are available for numeric data only.


1. Looking at Table 3.23(A) and Chart 3.14, we can find that when the

customer find that the complexity level is high, then 39.3% of the

customers replied that he is totally dissatisfied and want to switch over,

well where as when the complexity level is Less Complex, 46.6% of the

Respondents replied that I am totally satisfied and don‟t want to switch.

2. We can see the negative correlation (Table 3.23(B)) between two variables

which shows the less complex is the system the more is the satisfaction

level of the customer.




e302

3. Looking at Table 3.23(C), the significance value is 0.000, which indicates

high correlation between Complexity Level of a Bank Brand and

Satisfaction Level of the Customers with the Brand.

4. Looking at Table 3.23(D), where the Phi value can range between –1 to +1

depending on the negative association to positive association between two

variables, here the Phi value is „0.332‟, Contingency Coefficient (0.494)

values near to „0‟, which shows that the means of these two groups are not

associated with each other.

5. Further we can find the Cramer‟s V, value close to 0, which indicates that

the means of these two groups are different. The same is reveled using

Contingency Coefficient.

In order to conclude, looking at Table 3.23(B), (C) and (D), we reject the null

hypothesis and accept the alternate hypothesis H1_07: Complexity level of

operating a bank account and customer satisfaction with a bank brand is

correlated

3.3.8: RQ_08: Does a good trialability of the bank brand helps in

improving customer’s future purchase intentions?

Null Hypothesis:

H0_08: The trialable nature of the bank brand does not affect the customer‟s

future purchase intentions.


H1_08: The trialable nature of the bank brand does affect the customer‟s future

purchase intentions.

Here we will use the Trialability Level as mean of the total score given to the

following statements by bank customers to their respective banks:

My bank brand is… Adaptable (Q11.d)

My bank Branch is… Easily Reachable (Q14.a)




e303

Table 3.24(A): Purchase Intention for Future Products/Services and TRIALABILITY LEVEL

Cross tabulation

Purchase

Intention for

Future

Products/Services

TRIALABILITY LEVEL

Total 1 1.5 2 2.5 3 3.5 4 4.5 5

Strongly will not

Purchase

2 3 4 12 6 4 15 10 3 59

15% 21% 13% 24% 5% 3% 6% 4% 2% 6%

Might not

Purchase

0 1 2 1 15 15 16 20 4 74

0% 7% 6% 2% 12% 12% 7% 8% 2% 7%

Might Purchase

or Might Not

Purchase

8 4 11 4 42 31 56 46 25 227

62% 29% 34% 8% 32% 26% 24% 18% 13% 22%

Might Purchase 2 0 6 21 28 44 101 70 50 322

15% 0% 19% 41% 22% 36% 43% 27% 26% 31%

Strongly Purchase 1 6 9 13 39 27 48 114 111 368

8% 43% 28% 25% 30% 22% 20% 44% 58% 35%

Total 13 14 32 51 130 121 236 260 193 1050

100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Chart 3.15: Purchase Intention for Future Products/Services and TRIALABILITY LEVEL




e304

Table 3.24(B): Chi-Square Tests: (Purchase Intention for Future Products/Services

and TRIALABILITY LEVEL) Value df Asymp. Sig. (2-sided)





Table 3.24 (C): Directional Measures (Purchase Intention for Future

Products/Services and TRIALABILITY LEVEL) Lambda Value Asymp. Std.

Errora

Approx. Tb Approx. Sig.

Symmetric .092 .019 4.675 .000

Future Purchase Intention

Dependent

.132 .026 4.768 .000

TRIALABILITY Dependent .058 .021 2.674 .008

Table 3.24(D): Symmetric Measures (Purchase Intention for Future

Products/Services and TRIALABILITY LEVEL)

Value Asymp. Std.

Errora

Approx.

Tb

Approx.

Sig.

Nominal by

Nominal

Phi 0.423 0

Cramer's V 0.211 0

Contingency

Coefficient 0.389 0

Interval by

Interval Pearson's R 0.246 0.031 8.211 .000c

Ordinal by

Ordinal

Spearman

Correlation 0.267 0.03 8.969 .000

c



1. Looking at Table 3.24(A) and Chart 3.15, we can find that as the

Trialability Level of the Bank Brand increases, the satisfaction level of the

customer is also increases. For the Trialability Level of 5, 55% of the

customers responded that they will strongly purchase the products/services

offered by the same bank brand in near future.




e305

2. Looking at the chi-square significance of 0.000, we can conclude that the

relationship between the Trialability Level of a bank brand and the

customer satisfaction with the same bank brand is significant.

3. Looking at Table 3.24(C), lambda value of 0.132, we can say that 13% of

the times we can predict customers Future Purchase Intention when the

Trialability Level of a Bank Brand is known. Also the Contingency

Coefficient value close to 1 shows better correlation between these two

variables.

4. Looking at Phi value of 0.423, Cramer‟s V of 0.211 and Contingency

Coefficient value of 0.389, we can conclude that there is a moderate

association between Trialability of a Bank Brand and customer‟s intention

for purchase in future.

5. Looking at the value of Pearson Correlation (Table 3.24(D)), 0.267, we

conclude that these two variables are statistically correlated.

6. At last, we conclude using Table 3.24(C), significance value, that both the

variables are dependent on each other, thus we reject the null hypothesis

H0_08 and accept the alternate hypothesis H1_08: The trialable nature of

the bank brand does affect the customer‟s future purchase intentions.

3.3.9: RQ_09: Does the result demonstrability of bank brand

improve customer’s satisfaction?

Null Hypothesis:

H0_09: The result Demonstrability of a bank brand and Satisfaction level of the

customers are independent with each other.


H1_09: The result Demonstrability of a bank brand and Satisfaction level of the

customers are dependent on each other.

We will now compute a new variable „Result Demonstrability‟ by taking the

mean of the following statements by the customers:

My Bank Brand is … Transparent (Q11.c)




e306

I really like to talk about this bank to others (Q15.f)

I am proud to have others know I bank with this bank (Q15.i)

Let us recode the numeric value of mean of a Result Demonstrability into

nominal variable with following calculations (refer to Exhibit 3.7):

Exhibit 3.7: Recoding of Result Demonstrability (Mean)

Mean Value Variable Label

1 to 2 Poor

2 to 3 Low

3 to 4 Medium

4 to 5 High

Table 3.25(A): SATISFACTION LEVEL AND RESULT DEMONSTRABILITY

Cross tabulation

SATISFACTION LEVEL RESULT DEMONSTRABILITY

Total HIGH MEDIUM LOW POOR


want to switch

151 157 76 15 399

51.70% 37.30% 27.60% 24.20% 38.00%


improvements in service

136 252 179 33 600

46.60% 59.90% 65.10% 53.20% 57.10%


want to switch over

5 12 20 14 51

1.70% 2.90% 7.30% 22.60% 4.90%

Total 292 421 275 62 1050

100.00% 100.00% 100.00% 100.00% 100.00%


Looking at Table 3.25(A) and Chart 3.16, we can see that when the Result

Demonstrability of a brand is High, 51.7% of the customers feel totally satisfied

and don‟t want to switch, whereas when it is Poor 53% of the customer‟s want the

brand to improve in its services.




e307

Chart 3.16: SATISFACTION LEVEL AND RESULT DEMONSTRABILITY

Table 3.25(B): Chi-Square Tests (SATISFACTION LEVEL AND RESULT

DEMONSTRABILITY)






1. Looking at Table 3.25(B), we can find, Pearson Chi-Square sig. value is

0.000, which indicates high correlation between Satisfaction Level of

Customer and Result Demonstrability of a Bank Brand.

2. Using Table 3.25(B), sig. of 0.000 at 90% confidence level, we reject the

null hypothesis H0_09 and accept the alternate hypothesis H1_09: The

result Demonstrability of a bank brand and Satisfaction level of the

customers are dependent on each other.




e308

3.3.10: RQ_10: Is there any difference for the image of bank

services between high and low brand loyal customers?

Null Hypothesis:

H0_10: There is no difference between low and high brand loyal customer, for

the overall perception towards bank.


H1_10: There is a difference between low and high brand loyal customer, for the

overall perception towards bank.

Table 3.26(A): Group Statistics (Brand Loyalty and Total Bank Score)

BANK BRAND

LOYALTY LEVEL

N Mean Std.

Deviation

Std. Error

Mean

TOTAL BANK

SCORE

LOW 267 167.1049 29.53124 1.80728

HIGH 783 197.0996 27.2668 0.97444

Table 3.26(B): Independent Samples Test (Brand Loyalty and Total Bank Score)

t-test for Equality of Means

Variances t-value Df Sig. (2-

tailed)

Mean

Difference

Std. Error

Difference

TOTAL BANK

SCORE

Equal -

15.192

1048 0 -29.9948 1.97435

Unequal -

14.608

430.753 0 -29.9948 2.05324


1. In the above Table 3.26(B), we find the „p‟ value for the„t‟ test „0.000‟

assuming unequal variances in two populations. This value of 0.000

being less that our significance level of 0.10, we reject the null hypothesis

H0_10 and conclude that H1_10: There is a difference between low and

high brand loyal customer, for the overall perception towards bank.

2. We conclude that Total Bank Score (overall bank perception) does help

in increasing the brand loyalty among customers.




e309

3.3.11: RQ_11: Is there any difference for the bank’s brand

image between private and Public sector banks?

Null Hypothesis:

H0_11: There is no difference in the Private and Public Banking Sector‟s overall

perception of a bank.


H1_11: There is a difference in the Private and Public Banking Sector‟s overall

perception of a bank.

Where, Total Score = Mean (q1 to q15)

Table 3.27(A): Group Statistics (Total Bank Score of Private and Public Sector)

Bank Sector N Mean Std. Deviation Std. Error Mean

TOTAL BANK SCORE Private Sector 525 190.5029 31.63361 1.3806

Public Sector 350 186.2143 31.93963 1.70725

Table 3.27(B): Independent Samples Test (Total Bank Score of Private and Public Sector)


Variances t-

value

Df Sig. (2-

tailed)

Mean

Difference

Std. Error

Difference

TOTAL BANK

SCORE

Equal 1.957 873 0.051 4.28857 2.19139

Unequal 1.953 743.063 0.051 4.28857 2.19562

In the above table we find the „p‟ value for the„t‟ test 0.051 assuming unequal

variances in two populations. This value of 0.051 being less that our significance

level of 0.10, we reject the null hypothesis H0_11 and accept the alternate

hypothesis H1_11: There is a difference in the Private and Public Banking

Sector‟s overall perception of a bank.




e310

3.3.12: RQ_12: Is there any difference for the bank’s brand

image between private and foreign sector banks?

Null Hypothesis:

H0_12: There is no difference in the Indian Private sector and Foreign Sector‟s

overall bank perception


H1_12: There is a significant difference in the Indian Private sector and Foreign

Sector‟s overall bank perception.

Where, Total Score = Mean (q1 to q15, Refer to Questionnaire in Annexure 1)

Table 3.28(A): Group Statistics (Total Bank Score of Private and Foreign Sector)

Bank Sector N Mean Std.

Deviation

Std. Error

Mean

TOTAL BANK

SCORE

Private

Sector 525 190.5029 31.63361 1.3806

Foreign

Sector 175 192.8971 24.65814 1.86398

Table 3.28(B): Independent Samples Test (Total Bank Score of Private and Foreign

Sector)


Variances t-

value df

Sig. (2-

tailed)

Mean

Difference

Std. Error

Difference

TOTAL

BANK

SCORE

Equal -

0.913 698 0.362 -2.39429 2.62269

Unequal -

1.032 379.367 0.303 -2.39429 2.31959


1. In the above Table 3.28(B), we find the „p‟ value for the „t‟ test 0.303

assuming unequal variances in two populations. This value is higher than




e311

that our significance level of 0.10. And thus we accept the null hypothesis

H0_12: There is no difference in the Indian Private sector and Foreign

Sector‟s overall bank perception.

2. That means Indian private banks are operating with service standards of

Foreign Sector bank in India.

3.3.13: RQ13, RQ14, RQ15, RQ16, RQ17, RQ18:

Research Questions:

RQ_13: Does the bank customers are looking for a good care by financial

advisor in the bank brand?

RQ_14: Does the bank customers are looking for an affordable price and fees of

products/service in banks?

RQ_15: Does the bank customers are looking for a good brand image of the

bank?

RQ_16: Does the bank customers are looking for a wide range of

products/services?

RQ_17: Does the bank customers are looking for a Safety and Security of a bank

brand?

RQ_18: Does the bank customers are looking for a high returns on Deposits?

Null Hypothesis:

H0_13: A bank customer is not looking for „good care by financial advisor‟

H0_14: A bank customer is not looking for „affordable price and fees of

products/services‟

H0_15: A bank customer is not looking for „Brand/Image of the bank‟

H0_16: A bank customer is not looking for „Wide range of products/services‟

H0_17: A bank customer is not looking for „Safety and Security‟

H0_18: A bank customer is not looking for „Returns of Deposits‟


H1_13: A bank customer is looking for „good care by financial advisor‟

H1_14: A bank customer is looking for „affordable price and fees of

products/services‟




e312

H1_15: A bank customer is looking for „Brand/Image of the bank‟

H1_16: A bank customer is looking for „Wide range of products/services‟

H1_17: A bank customer is looking for „Safety and Security‟

H1_18: A bank customer is looking for „Returns of Deposits‟

We will now test the mean of this statement one-sample T-test for the average

mean of above 3.0

Ho (Null): 3.0

H1 (Alternative): > 3.0

Table 3.29(A): One-Sample Statistics (Bank Service Parameters Importance Level)

N Mean Std. Deviation Std. Error Mean

Good Care by Financial Advisor 1050 3.81 1.21 0.037

Price and Fees of Products/Services 1050 3.77 1.175 0.036

Brand/Image of the Bank 1050 3.99 1.08 0.033

Wide range of Products/Services 1050 3.8 1.115 0.034

Safety & Security 1050 4.19 1.079 0.033

Returns on Deposits 1050 3.89 1.144 0.035


Table 3.29(B): Independent Samples Test (Bank Service Parameters Importance Level)

As a bank customer following parameters

are important to me... Test value = 3

t df Sig. (2-

tailed)

Mean

Difference Rank

Good Care by Financial Advisor 21.8 1049 0 0.814 4

Price and Fees of Products/Services 21.222 1049 0 0.77 6

Brand/Image of the Bank 29.6 1049 0 0.987 2

Wide range of Products/Services 23.202 1049 0 0.798 5

Safety & Security 35.811 1049 0 1.192 1

Returns on Deposits 25.143 1049 0 0.888 3





e313


1. Looking at Table 3.29(A), we can find that the mean score for all services

parameters is more than 3 (neither agree nor disagree), which falls in the

region of Agree and Strongly Agree, so we can say that all the service

parameters mentioned in Table 3.29(A), are important to customers.

2. In the above Table one sample t-test (Table 3.29(B)), we can find that all

the factors play an important role to the bank customer while bank

selection. Where as we have found the highest mean differences in

Safety& Security of products/services, followed by Brand/Image of the

bank.

3. So here we conclude that for the bank customer Safety & Security and

Brand Image of the bank plays major role followed by returns on

deposits.

4. Whereas price and Fees of the products & services play the least

important role among all the parameters.

5. Overall we conclude that at 90% confidence level we reject null

hypothesis of H0_13, H0_14, H0_15, H0_16, H0_17 and H0_18 and accept

the alternate hypothesis H1_13, H1_14, H1_15, H1_16, H1_17 and H1_18.

3.3.14: RQ_19: Does the bank customer want to change his or

her bank account?

Null Hypothesis:

H0_19: Customers have seriously considered changing their bank account.


H1_19: Customers have never seriously considered changing their bank account.

Ho: 3.0

H1: > 3.0

= (X- )/ X




e314

Where, X= S/ n

X= 1.223/ 1050= 1.223/32.403= 0.03774

= (X- )/0.03774

= (3.90-3)/0.03774

= 23.847

The probability of getting a more extreme value of Z than 23.847 is less than

0.05. (Alternatively, the critical Z Value for a one-tailed test and a significance

level of 0.05 is 1.645, which is less than the calculated value of z.), Therefore,

the null hypothesis H0_19 is rejected; reaching the same conclusion arrived

using one sample T-test in Table 3.30(C) with significance value of 0.000 and

we will accept the alternate hypothesis Customers have never seriously

considered changing their bank account

So we can conclude that the mean of customer‟s response is above 3.0 (Neither

Agree/ Nor Disagree) for the statement of changing his/her bank.

Table 3.30(A): I have never seriously considered changing my bank

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

Strongly Disagree 76 7.2 7.2 7.2

Disagree 66 6.3 6.3 13.5

Neither Agree nor

Disagree 187 17.8 17.8 31.3

Agree 276 26.3 26.3 57.6

Strongly Agree 445 42.4 42.4 100

Total 1050 100 100




e315

Table 3.30(B): One-Sample Statistics (Customer’s Willingness To Change Bank

Account)

N Mean Std.

Deviation

Std. Error

Mean

I have never seriously considered

changing my bank 1050 3.9 1.223 0.038

Table 3.30(C): One-Sample T- Test (Customer’s Willingness To Change Bank Account)

Test Value = 3

t df

Sig. (2-

tailed)

Mean

Difference

90% Confidence

Interval of the

Difference

Lower Upper

I have never seriously

considered changing

my bank

23.913 1049 0 0.903 0.84 0.97

3.3.15: RQ_20: Does the bank customer consider themselves to

be a brand loyal customer with their bank brand.

Null Hypothesis

H20: Customers consider themselves to be highly loyal to their bank.


H20: Customers consider themselves to be highly loyal to their bank.

Chart 3.17:

(Customer’s

Willingness To

Change Bank

Account)




e316

We can write the hypothesis as:

Ho: 4.0

H1: > 4.0

We will now use the Z test to test the above hypothesis:

= (X- )/ X

Where, X= S/ n

X= 1.096/ 1050= 1.096/32.403= 0.03382

= (X- )/0.03774

= (4.09-4)/0.03382

= 26.611

The probability of getting more extreme value of Z than 26.611 is less than 0.05.

(Alternatively, the critical Z Value for a one-tailed test and a significance level

of 0.05 is 1.645, which is less than the calculated value of z.), Therefore, the null

hypothesis is rejected; reaching the same conclusion arrived using one sample T-

test in Table 3.31(C) with significance value of 0.007.

So we can conclude that the mean of customer rating is above 4.0 (Agree) for the

statement of „I consider myself to be a loyal customer of this bank‟.

Table 3.31(A): I consider myself to be a loyal customer of this bank


Percent

Cumulative

Percent

Valid


Disagree 62 5.9 5.9 9.6

Neither Agree nor

Disagree 166 15.8 15.8 25.4

Agree 280 26.7 26.7 52.1


Total 1050 100 100




e317

Table 3.31(C): One-Sample Test (Customer’s Loyalty Level)

Test Value = 4 t df Sig. (2-

tailed) Mean

Difference 90% Confidence

Interval of the

Difference Lower Upper I consider myself to be a

loyal customer of this

bank

2.704 1049 0.007 0.091 0.04 0.15

3.3.16: RQ_21: Does the customer are ready to switch to

competitor bank which offers attractive benefits.

Null Hypothesis:

H021: Customers will not switch to a competitor bank that offers more attractive

benefits.


H121: Customers will switch to a competitor bank that offers more attractive

benefits.

Table 3.31(B): One-Sample Statistics (Customer’s Loyalty Level)

N Mean Std.

Deviation Std. Error

Mean I consider myself to be a loyal customer of

this bank 1050 4.09 1.096 0.034

Chart

3.18:

Customer’

s Loyalty

Level




e318

We will now test the mean of this statement using Z-test and T-test for the

average mean of above 3.0, we can write the test as:

Ho: 3.0

H1: > 3.0

= (X- )/ X

Where, X= S/ n

X= 1.361/ 1050= 1.361/32.403= 0.0400

= (X- )/0.03774

= (3.47-3)/0.03382

= 13.897




the null hypothesis H021 is rejected; reaching the same conclusion arrived using

one sample T-test in Table 3.32(C) with significance value of 0.000 and accept

the alternate hypothesis: H121: Customers will switch to a competitor bank that

offers more attractive benefits.

So we can conclude that the mean of customer rating is above 3.0 (Neither

Agree/Nor Disagree), which is near to the „Agree‟ for the statement of „I will

switch to competitor bank that offers more attractive benefits‟.

Table 3.32(A): I will switch to a competitor bank, that offers more attractive benefits


Percent

Cumulative

Percent

Valid


Disagree 113 10.8 10.8 23.9

Neither Agree nor

Disagree 234 22.3 22.3 46.2

Agree 251 23.9 23.9 70.1


Total 1050 100 100




e319

3.3.17: RQ_22: Does the customers are ready to change their

bank brand in case of problems with existing bank’s services?

Null Hypothesis:

H022: Customers will not switch to a competitor bank when there are problems

with bank‟s services.


H122: Customers will switch to a competitor bank when there are problems with

bank‟s services.

Table 3.32(B): One-Sample Statistics (Customer’s Readiness To Switch To Competitor

Bank)

N Mean Std.

Deviation

Std. Error

Mean

I will switch to a competitor bank, that offers

more attractive benefits 1050 3.47 1.361

0.042

Table 3.32(C): One-Sample Test (Customer’s Readiness To Switch To Competitor Bank)

Test Value = 3

t df

Sig. (2-

tailed)

Mean

Difference

90% Confidence

Interval of the

Difference

Lower Upper

I will switch to a competitor

bank, that offers more

attractive benefits

11.11 1049 0 0.467 0.4 0.54

Chart 3.19:

Customer’s

Readiness To

Switch To

Competitor

Bank that

offers more

attractive

benefits




e320

The statement „I will switch to a competitor bank when there are problems with

this bank‟s service‟ was asked to bank customers. We will now test the mean of

this statement using Z-test and T-test for the average mean of above 3.0.

Ho: 3.0

H1: > 3.0

= (X- )/ X

Where, X= S/ n

X= 1.274/ 1050= 1.274/32.403= 0.03931

= (X- )/0.03931

= (3.70-3)/0.03931

= 17.871




Chart 3.20: Customer’s response to Problem with bank’s services




e321

the null hypothesis H0_22 is rejected; reaching the same conclusion arrived

using one sample T-test in Table 3.33(C) with significance value of 0.000 and

accept the alternate hypothesis H1_22: Customers will switch to a competitor

bank when there are problems with bank‟s services.

So we can conclude that the mean of customer rating is above 3.0 (Agree) for

the statement of „I will switch to competitor bank when there are problems with

this bank services‟. So the customer does consider brand image of the bank

while banking and takes note of its regularity in services.

Table 3.33(A): Frequency Statistics (I will switch to a competitor bank when there

are problems with this bank's services)


Percent Cumulative

Percent

Valid

Strongly Disagree 80 7.6 7.6 7.6 Disagree 115 11 11 18.6

Neither Agree nor

Disagree 232 22.1 22.1 40.7

Agree 238 22.7 22.7 63.3 Strongly Agree 385 36.7 36.7 100

Total 1050 100 100 Source: Primary data collected for the study

Table 3.33(B): One-Sample Statistics (I will switch to a competitor bank when there

are problems with this bank's services)

N Mean Std.

Deviation Std. Error

Mean I will switch to a competitor bank when there

are problems with this bank's services 1050 3.7 1.274 0.039

Table 3.33(C): One-Sample Test (I will switch to a competitor bank when there are

problems with this bank's services)

Test Value = 3

t df

Sig. (2-

tailed) Mean

Difference

90% Confidence

Interval of the

Difference

Lower Upper

I will switch to a

competitor bank when

there are problems with

this bank's services

17.763 1049 0 0.698 0.63 0.76





e322

3.3.18: RQ_23: Is the customers satisfaction level same for

various banks?

Null Hypothesis:

H0_23: „Customer Satisfaction Level‟ is same for all seven bank brand name.


H1_23: „Customer Satisfaction Level‟ is not same for all seven bank brand name.

Table 3.34(A): Satisfaction Level * Bank Brand Name Cross tabulation

Customer

Satisfaction

Level

Bank Brand

Total AXIS HDFC ICICI CITI HSBC BOB SBI

I am totally

satisfied &

don't want

to switch

92 70 61 33 31 55 57 399

52.60% 40.00% 34.90% 33.00% 41.30% 31.40% 32.60% 38.00%

I am

satisfied but

expect more

improveme

nts in

service

75 101 99 64 36 114 111 600

42.90% 57.70% 56.60% 64.00% 48.00% 65.10% 63.40% 57.10%

I am totally

dissatisfied

and want to

switch over

8 4 15 3 8 6 7 51

4.60% 2.30% 8.60% 3.00% 10.70% 3.40% 4.00% 4.90%

Total

175 175 175 100 75 175 175 1050

100.00

%

100.00

%

100.00

%

100.00

%

100.00

%

100.00

%

100.00

%

100.00

%




e323

Table 3.34(B): Chi-Square Tests (Customer’s Satisfaction Level of Various Banks)






`

Table 3.34(C): Directional Measures (Customer’s Satisfaction Level of Various Banks)

Value Asymp.

Std. Errora

Approx.

Tb

Approx.

Sig.

Lambda:

Symmetric 0.048 0.017 2.749 0.006

With, ‟Please tike one of the

following statement about your

Bank‟ Dependent

0.038 0.028 1.317 0.188

With „Bank Name‟ Dependent 0.053 0.016 3.174 0.002

Goodman and

Kruskal Tau:

Please tike one of the following

statement about your Bank

Dependent

0.023 0.008 .000c

Bank Name Dependent 0.007 0.002 .000c

Chart 3.21: Customer’s Satisfaction Level of Various Banks

Chart 3.21 Customers Satisfaction level and bank brand

Chart 3.21: Customer’s Satisfaction Level and Bank Brand




e324

Table 3.34(D): Symmetric Measures (Customer’s Satisfaction Level of Various Banks)

Value Approx. Sig.


Cramer's V 0.139 0 Contingency Coefficient 0.192 0



1. Looking at Table 3.34(A), we can find that Axis bank score highest on

the satisfaction level by 52.6% of the customers responded that they are

totally satisfied and don‟t want to switch, where as in the statement

related to improvements in existing services, Bank of Baroda Scores the

Highest with 65%, and in the statement regarding totally dissatisfied

customers, HSBC followed by ICICI Bank scores the highest.

2. The Chi-square test (Table 3.34(B)) revealed the significant association

between the „Bank Brand‟ and the „Customer‟s Satisfaction Level‟. From

the Chi-square test output table we see that a significance level of 0.000

(Pearson‟s) has been achieved. Thus we conclude that at 90% confidence

level, Bank Brand Name and Customer Satisfaction Level are associated

significantly with each other.

3. Again, Looking at Table 3.34(C), where Phi value can range between –1

to +1 depending on the negative association to positive association

between two variables, here the Phi value is „0.196‟, which indicates that

the group means are different for Bank Brand Name and Its Customer

Satisfaction Level, Where as values for Cramer‟s V (0.139) and

Contingency Coefficient (0.192) values near to „0‟ shows group means

for Customer Satisfaction are different for all seven type of banks. Also

form the lambda asymmetric value we can conclude that there is

sufficient difference between Satisfaction Level and Bank Brand Name.

4. Looking at the significance level of chi-square test is 0.000 (Table

3.34(B)), we reject the null hypothesis H0_23 and accept the alternate

hypothesis, H1_23: „Customer Satisfaction Level‟ is not same for all

seven bank brand name.




e325

5. This leads us to conclude that Bank Brand Name does have an effect on

the customer‟s satisfaction level.

3.3.19: RQ_24: Does the Education Qualification have any impact

on the bank brand selection?

Null Hypothesis:

H0_24: Customer‟s Bank Brand Preference does not change with the Education

Qualification of customer.


H1_24: Customer‟s Bank Brand Preference does change with the Education


Table 3.35(A): Bank Name * Education Qualification Crosstabulation

Education Qualification

Total

S

S

C

H

S

C

DIPLOM

A

GRADUA

TE

POST

GRADUA

TE

DOCTORA

TE

B A N K

N A M E

A

X

I

S

4 10 14 89 57 1 175

2.30

% 5.70

% 8.00% 50.90% 32.60% 0.60%

100.00

%

H

D

F

C

2 8 0 79 86 0 175

1.10

% 4.60

% 0.00% 45.10% 49.10% 0.00%

100.00

%

I

C

I

C

I

1 12 6 79 75 2 175

0.60

% 6.90

% 3.40% 45.10% 42.90% 1.10%

100.00

%

C

I

T

I

0 6 8 51 35 0 100

0.00

% 6.00

% 8.00% 51.00% 35.00% 0.00%

100.00

%

H

S

B

C

8 13 3 33 16 2 75

10.7

0% 17.30

% 4.00% 44.00% 21.30% 2.70%

100.00

%

B

O

B

6 9 15 89 55 1 175 3.40

% 5.10

% 8.60% 50.90% 31.40% 0.60%

100.00

%

S

B

I

2 7 6 62 94 4 175 1.10

% 4.00

% 3.40% 35.40% 53.70% 2.30%

100.00

%

Total 23 65 52 482 418 10 1050

2.20

% 6.20

% 5.00% 45.90% 39.80% 1.00%

100.00

% Source: Primary data collected for the study




e326

Chart 3.22: Bank Brand and Education Qualification of Customers

Table 3.35(B): Chi-Square Tests (Bank Brand and Education Qualification)






Table 3.35(C): Directional Measures (Bank Brand and Education Qualification)

Value Asymp.

Std. Errora Approx.

Tb Approx.

Sig.

Nominal by

Nominal

Lambda

Symmetric 0.06 0.018 3.238 0.001 Bank Name

Dependent 0.055 0.016 3.252 0.001

Education

Qualification

Dependent 0.069 0.03 2.182 0.029

Goodman

and Kruskal

tau

Bank Name

Dependent 0.015 0.003 .000

c

Education

Qualification

Dependent 0.024 0.007 .000c

a. Not assuming the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

c. Based on chi-square approximation

Table 3.35(D): Symmetric Measures (Bank Brand and Education Qualification)

Value Approx. Sig.

Nominal by Nominal Phi 0.324 0 Cramer's V 0.145 0 Contingency Coefficient 0.308 0





e327


1. The Chi-square test (Table 3.35(B)) revealed the significant association

between the „Bank Brand Name‟ and the „Customer‟s Education

Qualification‟. From the Chi-square test output Table 3.35(B), we see that a

significance level of 0.000 (Pearson‟s) has been achieved. Thus we

conclude that at 90% confidence level, Bank Brand Name and Education

Qualification are associated significantly with each other.

2. Again looking at Table 3.35(D), where the Phi value can range between –1


two variables, here the Phi value is „0.324‟, which shows a moderate

positive association between Bank Brand Name and Education

Qualification of Customer, Where as values for Cramer‟s V (0.145) and

Contingency Coefficient (0.308) values near to „0‟ shows mean for bank

brand name and education qualification is different so we can say that both

variables are having moderate correlation.

3. Also form the lambda asymmetric value (Table 3.35(C)); we can conclude

that there is low level of relationship between two variables, leads to same

conclusion as low lambda value indicates all the means are different.

4. This leads us to conclusion that Customer‟s Bank Brand Preference does

change with the Education Qualification of customer moderately.

5. In order to conclude looking at chi-square value of 0.000 in Table 3.35(B),

we reject the null hypothesis H0_24, and accept the alternate hypothesis

H1_24: Customer‟s Bank Brand Preference does change with the Education


3.3.20: RQ_25: Does the Age of customer plays any role in the

brand selection of banks?

Null Hypothesis:

H0_25: Bank Brand Preference does not change with the Age of customer.


H1_25: Bank Brand Preference does change with the Age of customer.




e328

Table 3.36(A): Bank Brand and Customer's Age Cross tabulation

Bank

Brand

Customer's Age (Years)

Total 10 to 20

Yrs

21 to 30

Yrs

31 to 40

Yrs

41 to 50

Yrs

More Than50

Yrs

AXIS 4 126 32 11 2 175

21% 17% 17% 14% 7% 17%

HDFC 2 141 19 11 2 175

11% 19% 10% 14% 7% 17%

ICICI 1 143 21 7 3 175

5% 20% 11% 9% 10% 17%

CITI 7 38 38 15 2 100

37% 5% 20% 19% 7% 10%

HSBC 3 41 16 10 5 75

16% 6% 8% 13% 17% 7%

BOB 0 123 30 14 8 175

0% 17% 16% 18% 28% 17%

SBI 2 120 34 12 7 175

11% 16% 18% 15% 24% 17%

Total 19 732 190 80 29 1050

100% 100% 100% 100% 100% 100%

Table 3.36(B): Chi-Square Tests (Bank Brand and Age group)






Chart 3.23: Bank Brand and Age group of Customer




e329

Table 3.36(C): Directional Measures (Bank Brand and Age group)

Value Asymp. Std.

Errora

Approx.

Tb

Approx.

Sig.

Lambda

Symmetric 0.03 0.016 1.835 0.066

Bank Name

Dependent 0.041 0.022 1.835 0.066

Customer's Age

Dependent 0 0 .c .c

Goodman and

Kruskal tau

Bank Name

Dependent 0.013 0.003 .000d

Customer's Age

Dependent 0.048 0.011 .000d

Table 3.36(D): Symmetric Measures (Bank Brand and Age group)

Value Approx. Sig.

Nominal by Nominal

Phi 0.314 0

Cramer's V 0.157 0




1. Looking at Table 3.36(A), we can find that for the age group of more than

50 Yrs., Bank of Baroda, followed by SBI are used highest, where as in the

young age group of 10 to 20 Yrs., CITI bank Score highest with 37%, in

the Youth group of 21 to 30 Yrs., all banks score more or less equal.

2. Again looking at Table 3.36(D), where the Phi value can range between –1


two variables, here the Phi value is „0.324‟, which shows a moderate

positive association between Bank Brand Name and Age group of

Customer, Where as values for Cramer‟s V (0.157) and Contingency

Coefficient (0.299) values near to „0‟ shows indicates mean for bank brand

name and education qualification is different so we can say that both

variables are having moderate correlation.

3. Also form the lambda asymmetric value (Table 3.36(C)); we can conclude

that there is low level of relationship between two variables, leads to same

conclusion as low lambda value indicates all the means are different.




e330

4. Looking at Table 3.26(B), the significance value of 0.000, we reject the

null hypothesis H0_25 and accept the alternate hypothesis H1_25: Bank

Brand Preference does change with the Age of customer.

3.3.21: RQ_26: Does the customers are ready to use future

products and services offered by their bank?

Null Hypothesis:

H0_26: Brand Loyal customers are not ready to use new products and services

offered by the bank in near future.


H1_26: Brand Loyal customers are ready to use new products and services

offered by the bank in near future.

Table 3.37(A): Future Purchase Intention and Bank Loyalty Cross Tabulation


Total

I will Purchase other

products/services

offered by this bank in

near future

Strongly

Disloyal Disloyal

Neither

Loyal nor

Disloyal

Loyal Strongly

Loyal

Strongly will not

Purchase

6 3 9 13 28 59

10.20% 5.10% 15.30% 22.00% 47.50% 100.00%

Might not Purchase 9 12 19 11 23 74

12.20% 16.20% 25.70% 14.90% 31.10% 100.00%

Might Purchase or

Might Not Purchase

17 23 52 53 82 227

7.50% 10.10% 22.90% 23.30% 36.10% 100.00%

Might Purchase 4 14 64 132 108 322

1.20% 4.30% 19.90% 41.00% 33.50% 100.00%

Will Strongly

Purchase

3 10 22 71 262 368

0.80% 2.70% 6.00% 19.30% 71.20% 100.00%

Total 39 62 166 280 503 1050

3.70% 5.90% 15.80% 26.70% 47.90% 100.00%





e331

Chart 3.24: Future Purchase Intention and Brand Loyalty

Table 3.37(B): Chi-Square Tests (Future Purchase Intention and Brand Loyalty)


Pearson Chi-Square 212.812 16 0




Table 3.37(C): Symmetric Measures (Future Purchase Intention and Brand Loyalty)

Value Approx. Sig.







e332

Table 3.37(D): Directional Measures (Future Purchase Intention and Brand Loyalty)

Lambda Value Asymp. Std.

Errora

Approx. Tb Approx.

Sig.

Symmetric 0.125 0.022 5.367 0

I will Purchase other products/services

offered by this bank in near future

Dependent

0.191 0.024 7.201 0

I consider myself to be a loyal customer

of this bank Dependent

0.044 0.028 1.551 0.121



1. Looking at Table 3.37(A), we can find that 71.20% of the Strongly Loyal

customer will strongly Purchase the products/services offered by the same

bank brand in near future.



two variables, here the Phi value is „0.450‟, Contingency Coefficient

(0.411) values near to „1‟, which shows a good positive association

between Brand Loyalty and Customer‟s Readiness to purchase

products/services in near future,

3. Also the lambda value of 0.191 (Table 3.37(D)), indicates that 19% of the

times we can predict the customer‟s intention to purchase the

products/services in near future when the Customer Loyalty Level is

known.

4. Looking at Table 3.37(B), with significance value of 0.000, which fall in

the acceptance region at 90% confidence level, we reject the null

hypothesis H0_26 and accept the alternate hypothesis H1_26: Brand Loyal

customers are ready to use new products and services offered by the bank

in near future.




e333

3.3.22: RQ_27, RQ_28, RQ_29

Research Questions:

RQ_27: Does the Bank Brand name has any effect on the total score given for its

services

RQ_28: Does the Total Score for the bank keeps changing with the location of

the bank?

RQ_29: Does the Bank Brand and Location of the Bank (City) has any impact

on the total score given to the bank?

Null Hypothesis:

H0_27: The Bank Brand has no effect on overall perception of the bank.

H0_28: The Location of Bank (City) has no effect on overall perception of the

bank.

H0_29: The Bank Brand along with Location of Bank (City) has no effect on

overall perception of the bank.


H1_27: The Bank Brand does have effect on overall perception of the bank.

H1_28: The Location of Bank (City) does have effect on overall perception of

the bank.

H1_29: The Bank Brand along with Location of Bank (City) does have effect on

overall perception of the bank.

TSB1=TSB2=TSB3=TSB4=TSB5=TSB6=TSB7.

Where, TSB= TOTAL Score of Bank

Total Score = Mean (q1 to q15), Refer to the questionnaire

Table 3.38(A): Group Statistics (Bank Brand and Total Brand Score)

Bank Name



TOTAL BANK SCORE 193.48 192.51 185.51 195.3 189.69 187.88 184.55





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I. One-Way ANOVA with Total Bank Score as Dependent Variable and

Bank Brand as the Factor

We will calculate the variable „Total Bank Score‟ = Mean (q1 to q15, Refer to

the questionnaire)

Table 3.38(B): One-Way ANOVA -(Bank Brand and Total Brand Score)

TOTAL BANK SCORE

Sum of

Squares

df Mean

Square

F Sig.

Between

Groups

15257.83 6 2542.971 2.714 0.013

Within Groups 977251.9 1043 936.962

Total 992509.7 1049

From the output of One-Way ANOVA Table 3.38(B), in the last column the

significance of F-test is found to be 0.013, which indicates a confidence level of

98%, the F-test proves the model is significant. In other words the Total Score

of Bank given by the customers for the seven banks are significantly different

from each other.

Thus we reject null hypothesis Ho_27, at 90% confidence level and accept the

alternate hypothesis H1_27 to conclude that The Bank Brand does have an

effect on overall perception of the bank for its various services that means:

TSB1 TSB2 TSB3 TSB4 TSB5 TSB6 TSB7.

II. ANOVA (Randomized Block Design) with Block factor as City

Table 2: Randomized Block Design: Bank Brand as Dependent Variable and

Total Bank Score as Independent Variable and „City (Location)‟ as the Block.




e335

Table 3.38(C): Bank Name and mean of Total Score


City Mean Mean Mean Mean Mean Mean Mean

AHMEDABAD 190.24 194.98 184.9 193.86 185.8 187.42 180.52

VADODARA 190.44 205.44 196.92 194.08 197.48 198.68 186.76

SURAT 189.52 172.28 190.76 199.4 NA 186.16 181.4

RAJKOT 205.56 193.72 171.04 NA NA 200.36 183.04

BHAVNAGAR 195.44 196.04 187.72 NA. NA 177.88 201.72

JAMNAGAR 192.92 190.16 182.36 NA NA 177.24 177.88

Mean 193.48 192.51 185.51 195.3 189.69 187.88 184.55

Source: Primary data collected for the study, NA: Not Available

Looking at Table 3.38(C), we can observe that SBI scores the least on the table,

looking at the combined mean of all CITY, but if we see in Bhavnagar it scores

the highest, because of its old presence in the market with its old brand name

„STATE BANK of SAURASHTRA‟. Whereas we can find in Ahmadabad and

Vadodara, HDFC Bank Score high, and in Surat, CITI bank Scores high on the

table.

Table 3.38(D): Tests of Between-Subjects Effects(Bank Brand and Total Brand

Score)

Dependent Variable: TOTAL BANK SCORE

Source Type III Sum of

Squares df

Mean

Square F Sig.

Corrected Model 69708.099a 34 2050.238 2.255 0

Intercept 3.05E+07 1 3.05E+07 33556.45 0

BANK BRAND 13449.05 6 2241.507 2.465 0.023

CITY 12755.41 5 2551.081 2.806 0.016

BANK BRAND *

CITY 41583.95 23 1807.998 1.989 0.004

Error 922801.6 1015 909.164

Total 3.87E+07 1050

Corrected Total 992509.7 1049

a. R Squared = .070 (Adjusted R Squared = .039)




e336

In the output table of randomized block design problem (Table 3.38(D)), the last

column gives the result of an F-test as 0.023 for Bank Brand and 0.016 for City

(Location). Since the significance of F is less than 0.10, for Bank Brand we

reject the null hypothesis (H0_27) and accept the alternate hypothesis (H1_27) to

conclude that the Bank Brand has significant effect on overall perception of the

bank.

Also the „city (location)‟ does have effect on overall perception of the bank. So

we reject the H0_28 and accept the alternate hypothesis H1_28.

The Bank Brand and „City (Location)‟ together does significant effect on overall

perception of the bank for its services, so we reject the null hypothesis H0_29

and accept the alternate hypothesis H1_29.

3.3.23: RQ_30: Does the sales promotion have any relationship

with customer’s loyalty?

The answer for the above question can be tested by defining the hypothesis as

below:

Null Hypothesis:

H0_30: Sales Promotion does not influence Customer Loyalty


H1_30: Sales Promotion does influence Customer Loyalty

I. One-Way ANNOVA (CustLoyalty By Promotion):

The following questions were asked to customers:

I consider myself to be a loyal customer of this bank (Dependent Variable)

My bank, Offers fun promotion (independent variable)

The researcher has used one-way-ANOVA Test (Table 3.30), in order to find the

answer for the Research Question RQ_30.




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Table 3.39: ANOVA (Sales Promotion and Customer Loyalty)


Sum of Squares df Mean Square F Sig.

Between Groups 81.452 4 20.363 18.068 0

Within Groups 1177.771 1045 1.127

Total 1259.223 1049

From the output of One-Way ANOVA Table 3.39, in the last column the


100%, the F-test proves the model is significant. In other words the Fun

Promotions offered by bank does have an impact on Customer Loyalty.

Thus we reject the null hypothesis H0_ 31, at 90% confidence level and conclude

that Customer Loyalty is affected by fun promotions offered by bank and we

accept the alternate hypothesis H1_30.

3.3.24: RQ_31, RQ_32, RQ_33, RQ_34, RQ_35, RQ_36, RQ_37

Research Questions:

RQ_31: Does the Bank Branch Timings have any relationship with Customer

Satisfaction?

RQ_32: Does the Seating and Waiting arrangements at branch have any

relationship with customer satisfaction?

RQ_33: Does the staff response have any relationship with customer

satisfaction?

RQ_34: Does the Branch Timings and Waiting Arrangements together have any

effect on customer satisfaction?

RQ_35: Does the Branch Timings and Staff Response together has any effect on

customer satisfaction?

RQ_36: Does the Seating Arrangement and Staff Response together has any

relationship with customer satisfaction?

RQ_37: Does the Branch Timings, Staff Response and Waiting Arrangements

together has any relationship with customer satisfaction?




e338

Null Hypothesis:

H0_31: „Bank (Branch) Timings‟ does not influence the „Customer satisfaction‟

H0_32: „Seating and Waiting Arrangements at branch‟ does not influence the

„Customer Satisfaction‟

H0_33: „Staff Response‟ does not influence the „Customer Satisfaction‟.

H0_34: „Bank (Branch) Timings‟ & „Seating & Waiting Arrangements‟ together

does not influence the „Customer Satisfaction‟

H0_35: „Bank (Branch) Timings‟ & „Staff Response‟ together does not influence

the „Customer Satisfaction‟

H0_36:„Seating & Waiting Arrangements‟ & „Staff Response‟ together does not

influence the „Customer Satisfaction‟

H0_37: „Bank (Branch) Timings‟, „Staff Response‟ and „Seating & Waiting

Arrangement‟ all together does not influence the „Customer Satisfaction‟


H1_31: „Bank (Branch) Timings‟ does influence the „Customer satisfaction‟

H1_32: „Seating and Waiting Arrangements at branch‟ does influence the


H1_33: „Staff Response‟ does influence the „Customer Satisfaction‟.

H1_34: „Bank (Branch) Timings‟ & „Seating & Waiting Arrangements‟ together

does influence the „Customer Satisfaction‟

H1_35: „Bank (Branch) Timings‟ & „Staff Response‟ together does influence the


H1_36:„Seating & Waiting Arrangements‟ & „Staff Response‟ together does

influence the „Customer Satisfaction‟

H1_37: „Bank (Branch) Timings‟, „Staff Response‟ and „Seating & Waiting

Arrangement‟ all together does influence the „Customer Satisfaction‟

I. Full Factorial ANOVAs Design with Customer Satisfaction (CS) as

Dependent and Bank Timing (BT),Seating Arrangement (SA) and Staff

Response (SR) as Factors




e339

We want to test hypothesis at 90% confidence level

Table 3.40(A): Dependent Variable (Bank Branch Facilities and Customer

Satisfaction) Count

Please tike one of the following statement

about your Bank (Customer Satisfaction)


want to switch 399


improvements in service 600


want to switch over 51

Total 1050

Table 3.40(B): Frequency: Between-Subjects Factors (Bank Branch Facilities and

Customer Satisfaction)

STATEMENTS CODE Value Label FREQ. (n)

Banking Hours/Branch Timings

1 Strongly Disagree 59

2 Disagree 72

3 Neither Agree nor

Disagree 265

4 Agree 422

5 Strongly Agree 232

Seating/Waiting Arrangements


2 Disagree 109

3 Neither Agree nor

Disagree 207

4 Agree 388


Staff Response


2 Disagree 68

3 Neither Agree nor

Disagree 245

4 Agree 350






e340

Table 3.40(C): Univariate ANOVA: Tests of Between-Subjects Effects (Bank Branch Facilities

and Customer Satisfaction)

Dependent Variable: Please tike one of the following statement about your Bank (Customer

Satisfaction)

Source Type III Sum of

Squares df

Mean

Square F Sig.

Intercept

656.198 1 656.198 488.328 0.001

BRNACH TIMINGS (BT)

0.931 4 0.233 0.725 0.601

SEATING/WAITING

ARRANGEMENT (SA)

0.685 4 0.171 0.235 0.915

STAFF RESPONSE (SR)

7.457 4 1.864 4.631 0.025

BT * SA

9.37 16 0.586 1.402 0.159

BT* SR

2.595 16 0.162 0.389 0.982

SA * SR

11.202 16 0.7 1.677 0.066

BT* SA * SR

25.079 47 0.534 1.999 0.000

In the output Table 3.40(C), of randomized block design problem, the last

column gives the result of an F-test. As we take 90% as the confidence level,

we can accept any significant value < 0.10, looking at the above table we can

find that only 'SR‟ alone and „SA*SR‟ and BT*SA*SR all together has

significance value of 0.000 therefore we reject the null hypothesis H0_33,

H0_36, H0_37, and accept the alternate hypothesis, H1_33, H1_36, H1_37, in

order to conclude following statements:

„Staff Response‟ does influence the „Customer Satisfaction‟.

„Seating & Waiting Arrangements‟ & „Staff Response‟ together does

influence the „Customer Satisfaction‟.

„Bank (Branch) Timings‟, „Staff Response‟ and „Seating & Waiting

Arrangement‟ all together does influence the „Customer Satisfaction‟

Where as we accept other null hypothesis like, H0_31, H0_32, H0_34, H0_35

and conclude the following statements:

„Bank (Branch) Timings‟ does not influence the „Customer satisfaction‟.

„Seating and Waiting Arrangements at branch‟ does not influence the

„Customer Satisfaction‟.




e341

„Bank (Branch) Timings‟ & „Seating & Waiting Arrangements‟ together

does not influence the „Customer Satisfaction‟.

„Bank (Branch) Timings‟ & „Staff Response‟ together does not influence

the „Customer Satisfaction‟.

To conclude we can say that staff response only plays the major role in

customer satisfaction at various Banks‟ Branches.

3.3.25: RQ_38: Does the various facilities of bank branch has

any effect on the customer satisfaction?

Null Hypothesis:

H0_38: Customer Satisfaction is not affected by the various facilities of the

Bank Branch.


H1_38: Customer Satisfaction is affected by the various facilities of the Bank

Branch.

I. One-Way ANOVA: with „Customer Satisfaction‟ as dependent and AVG.

BRANCH FACILITY as factor.

AVG_BRANCHFACILIT is a variable, which is computed by the mean of all

Banks‟ Branch Facilities (Refer to Q.14 in the questionnaire)

We will test the above hypothesis at 90% Confidence Level.

Table 3.40(D): ANOVA (Bank Branch Facilities and Customer Satisfaction)

Please tike one of the following statement about your Bank

Sum of

Squares Df

Mean

Square F Sig.

Between

Groups 50.745 39 1.301 4.629 0

Within Groups 283.918 1010 0.281

Total 334.663 1049




e342

From the output of One-Way ANOVA Table 3.40(D), in the last column the


100%, the F-test proves the model is significant.

Thus we reject null hypothesis H0_38 at 90% confidence level and accept the

alternate hypothesis H1_38, „Customer Satisfaction Statement‟ is statistically

affected by the various facilities of the Bank Branch.

3.3.26: RQ_39, RQ_40, RQ_41, RQ_42, RQ_43, RQ_44, RQ_45,

RQ_46, RQ_47

Research Questions:

RQ_39: Does the Bank Balance have any relationship with customer‟s

satisfaction?

RQ_40: Does the Gender have any relationship with customer‟s satisfaction?

RQ_41: Does the Education have any relationship with customer‟s satisfaction?

RQ_42: Does the age of customer have any relationship with his satisfaction?

RQ_43: Does the customer‟s occupation have any relationship with his

satisfaction?

RQ_44: Does the Bank Brand have any kind of relationship with customer‟s

satisfaction?

RQ_45: Does the City (location) have any relationship with customer‟s

satisfaction?

RQ_46: Does the Bank Sector have any relationship with customer‟s

satisfaction?

RQ_47: Does the past experience of customer with the bank have any

relationship with customer‟s satisfaction?

Null Hypothesis:

H0_39: Customer‟s Satisfaction level does not change with avg. balance in his

bank account.

H0_40: Customer‟s Satisfaction level does not change with Gender

H0_41: Customer‟s Satisfaction level does not change with Education

H0_42: Customer‟s Satisfaction level does not change with Age




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H0_43: Customer‟s Satisfaction level does not change with Occupation

H0_44: Customer‟s Satisfaction level does not change with Bank Name

H0_45: Customer‟s Satisfaction level does not change with City

H0_46: Customer‟s Satisfaction level does not change with Bank Sector

H0_47: Customer‟s Satisfaction level does not change with his experience with

the bank.


H1_39: Customer‟s Satisfaction level does not change with avg. balance in his

bank account.

H1_40: Customer‟s Satisfaction level does change with Gender

H1_41: Customer‟s Satisfaction level does change with Education

H1_42: Customer‟s Satisfaction level does change with Age

H1_43: Customer‟s Satisfaction level does change with Occupation

H1_44: Customer‟s Satisfaction level does change with Bank Name

H1_45: Customer‟s Satisfaction level does change with City

H1_46: Customer‟s Satisfaction level does change with Bank Sector

H1_47: Customer‟s Satisfaction level does change with his experience with the

bank.

We will now recode the Interval Scale variable Customer Satisfaction (q.1. I

consider myself to be a loyal customer of this bank…) in the Nominal scale

Variable (refer to Exhibit 3.8).

Exhibit 3.8: Recoding of Customer Satisfaction

Old Variable (q.1) New Variable (Satisfaction Level)

5 High

4

3 Medium

2 Low

1




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Low10%

Medium16%

High74%

Satisfaction Level

of Customer

Low

Medium

High

Table 3.41(A): Satisfaction Level (customers)


Percent

Cumulative

Percent

Valid LOW 101 9.6 9.6 9.6

MEDIUM 166 15.8 15.8 25.4

HIGH 783 74.6 74.6 100.0

Total 1050 100.0 100.0 ]

Chart 3.25: Satisfaction Level of Customers

Exhibit 3.9: Coding of Satisfaction Level and Factors

Label Variable Value

Y= Satisfaction Level

X1= Avg. balance in Bank Account

X2= Gender

X3= Education

X4= Age

X5= Occupation

X6= Bank Brand

X7= City (Location)

X8= Bank Sector




e345

Table 3.41(B): Inter correlation (Customer Satisfaction Level and affecting Variables)

Y X1 X2 X3 X4 X5 X6 X7 X8

Y

Pearson

Correlat

ion 1

-

0.

04

0.02

9

-

.08

9**

-

0.04

3

-

0.05

5

0.0

27

-

.088**

.09

1**

Sig. (2-

tailed)

0.

2 0.34

0.0

04

0.16

5

0.07

3

0.3

74

0.00

4

0.0

03

X

1

Pearson

Correlat

ion 1

-

.079*

0.0

04

.343**

0.04

2

.19

1**

-

0.01

7

.12

8**

Sig. (2-

tailed) 0.01

0.8

86 0

0.17

4 0

0.59

3 0

X

2

Pearson

Correlat

ion 1

-

0.0

33

-

.064*

-

0.01

5

.08

7**

0.02

2

0.0

1

Sig. (2-

tailed)

0.2

8

0.03

8

0.62

4

0.0

05

0.47

9

0.7

41

X

3

Pearson

Correlat

ion 1

-

.081**

-

.112**

0.0

06

0.03

7

-

.10

6**

Sig. (2-

tailed)

0.00

8 0

0.8

57

0.22

5

0.0

01

X

4

Pearson

Correlat

ion 1

.270**

.10

5**

-

0.05

4

.18

3**

Sig. (2-

tailed) 0

0.0

01

0.07

8 0

X

5

Pearson

Correlat

ion 1

-

0.0

37

-

.113**

0.0

24

Sig. (2-

tailed)

0.2

3 0

0.4

4

X

6

Pearson

Correlat

ion 1

-

0.04

1

.65

3**

Sig. (2-

tailed)

0.18

8 0

X

7

Pearson

Correlat

ion 1

-

.26

4** Sig. (2-

tailed) 0

X

8

Pearson

Correlat

ion 1

Sig. (2-

tailed) **. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Listwise N=1050

Looking at the correlation Table 3.41(B), we can derive following conclusions:

1. The Customer satisfaction(Y) has significant relation with Education (X3),

City (X7) and Bank Sector(X8).

2. Avg. Balance in bank Account (X1) during the year has significant relation

with Gender (X2) Age (X4), Bank Brand (X6) and Bank Sector (X8).

3. Gender(X2) has significant relation with Bank Brand (X6)




e346

4. Education (X3) has significant relation with Age(X4) and Bank

Sector(X8).

5. Occupation (X5) has significant relation with City (X7).

Table 3.41(C): Multiple Regression Analysis of nine factors with Satisfaction Level


Coefficients

Standardized

Coefficients

t Sig.

B Std.

Error

Beta

1 (Constant) 3.04 0.154 19.744 0

Gender 0.038 0.048 0.024 0.789 0.431

Education

Qualification

-0.058 0.021 -0.085 -2.744 0.006

Customer's Age -0.033 0.029 -0.039 -1.151 0.250

Bank Name -0.01 0.013 -0.034 -0.8 0.424

Bank Sector 0.085 0.038 0.098 2.248 0.025

City -0.026 0.012 -0.071 -2.201 0.028

Customer's

Occupation

-0.042 0.021 -0.064 -2.001 0.046

Your total

experience with

this bank

-0.018 0.022 -0.028 -0.852 0.394

a. Dependent Variable: SATISFACTION LEVEL

Table 3.41(D): Excluded Variablesb (

nine factors with Satisfaction Level)

Model Beta

In

t Sig. Partial

Correlation

Collinearity

Statistics

Tolerance

1 Your average

annual balance

in your Bank

A/C

.a . . . 0

a. Predictors in the Model: (Constant), Your total experience with this bank, Education Qualification, City, Gender, Customer's Occupation, Bank Name, Customer's Age, Bank Sector

b. Dependent Variable: SATISFACTION LEVEL


As the multiple regression Table 3.41(C) shows all factors do affect satisfaction

level of customer except gender, customers‟ age, and bank name. At 90%

confidence level all the significance values less than 0.10 are accepted. Here we

have found one more important thing that bank name is not statistically

significant to influence customer‟s satisfaction level, but Bank Sector with

significant value of 0.025, does have influence on the customer‟s satisfaction




e347

level, that means customers do consider all private sector and public sectors

banks facilities are same within the sector but they do discriminate between the

sectors for their satisfaction level.

In order to conclude, Looking at the above multiple regression Table 3.41(C)

(significance value > 0.10) we accept following null hypothesis: H0_39,

H0_40, H0_42, H0_44, H0_47 and conclude following statements:

Customer‟s Satisfaction level does not change with avg. balance in his bank

account.

Customer‟s Satisfaction level does not change with Gender

Customer‟s Satisfaction level does not change with Age

Customer‟s Satisfaction level does not change with Bank Name

Customer‟s Satisfaction level does not change with his experience with the

bank.

Whereas for sig. value < 0.10 we reject following null hypothesis and accept

the following alternate hypothesis:H1_41, H1_43, H1_45, H1_46.

Customer‟s Satisfaction level does change with Education

Customer‟s Satisfaction level does change with Occupation

Customer‟s Satisfaction level does change with City

Customer‟s Satisfaction level does change with Bank Sector

3.3.27: RQ_48: Does the choice of customer for various service

factors in bank differ with the location (city).

Null Hypothesis:

H0_48: The choice of customer for various service factors in bank does not

differ with city (Location).


H1_48: The choice of customer for various service factors in bank does differ

with city (Location).

The researcher has coded each variable as per the below list (Exhibit 3.10):




e348

Exhibit 3.10: Recoding of Various Factors in Bank Services

Code Variables

Y= City (Location)

X1= Good Care by Financial Advisor

X2= Price and Fees of Products/Services

X3= Brand/Image of the Bank

X4= Wide Range of Products/Services

X5= Safety & Security

X6= Returns on Deposits

Table 3.42: Correlations (Various Service Factors and Location)

Y X1 X2 X3 X4 X5 X6

Y

Pearson Correlation 1 0.046 -0.031 -0.001 -0.019 0.021 -0.031

Sig. (2-tailed)

0.132 0.309 0.966 0.541 0.488 0.316

X1

Pearson Correlation

1 .564** .383

** .388

** .317

** .409

**

Sig. (2-tailed)

0 0 0 0 0

X2

Pearson Correlation

1 .336** .331

** .354

** .424

**

Sig. (2-tailed)

0 0 0 0

X3

Pearson Correlation

1 .426** .482

** .381

**

Sig. (2-tailed)

0 0 0

X4

Pearson Correlation

1 .467** .464

**

Sig. (2-tailed)

0 0

X5

Pearson Correlation

1 .521**

Sig. (2-tailed)

0

X6

Pearson Correlation

1

Sig. (2-tailed)





e349


Looking at the above table (Table 3.42) we derive following conclusions:

1. The „City‟ (Location) does not have any relationship with any variable. So

we accept the null hypothesis, H0_48 and conclude that, the choice of

customer for various service factors in bank does not differ with city

(Location). That means irrespective of the City (Location) the needs and

priorities of the bank customers remain same.

2. Good Care by financial advisor does have relationship with all other

variables.

3. Price and Fees of products and services does have relationship with all other

variables.

4. Brand Image of the bank does have relationship with all other variables.

5. Wide range of products & services does have relationship with all other

variables.

6. Safety & Security does have a relationship with all other variables.

7. Returns on Deposit do have a relationship with all other variables.

3.3.28: RQ_49, RQ_50, RQ_51, RQ_52:

Research Questions:

RQ_49: Does all the bankers have common view for responsibility of managing

their bank brand lies at branch.

RQ _50: Does all the bankers have common view for responsibility of

managing their bank brand lies at Regional Level.


their bank brand lies at National Level.


their bank brand lies at Top Management Level.

Null Hypothesis:

H0_49: Bankers do not believe that the responsibility of managing their brand

lies at branch level.




e350


lies at „Regional level‟.


lies at „National Level‟.


lies at „Top Management Level‟.


H1_49: Bankers do believe that the responsibility of managing their brand lies

at branch level.


at „Regional level‟.


at „National Level‟.


at „Top Management Level‟.

According to us the responsibility of managing our bank brand lies at... We will

now test the mean of this statement using Z-test and T-test for the average mean

of above 3.0

Ho: 3.0

H1: > 3.0

Table 3.43(A): One-Sample Statistics (Responsibility of Managing a Bank Brand)


Branch Level 35 4.46 0.886 0.15

Regional Level 35 3.66 0.968 0.164

National Level 35 3.51 1.314 0.222

Top Management Level 35 4.14 1.24 0.21 Source: Primary data collected for the study

Table 3.43(B): One-Sample Test (Responsibility of Managing a Bank Brand)

According to us the

responsibility of managing

our bank brand lies at…

Test Value = 3 Rank

t df Sig. (2-tailed)

Mean Difference

Branch Level 9.731 34 0 1.457 1

Regional Level 4.015 34 0 0.657 3

National Level 2.315 34 0.027 0.514 4 Top Management Level 5.452 34 0 1.143 2




e351

Looking at Table, 3.43(B), we reject the null hypothesis and conclude that H1:

> 3.0. And we will accept null hypothesis, H1_49, H1_50, H1_51, H1_52.

3.3.29: RQ_53: Whether the decision on the responsibility of

managing a bank brand changes within bank to bank?

Null Hypothesis:

H0_53: The views on the responsibility of managing the brand of bank do not

vary with different banks.


H1_53: The views on the responsibility of managing the brand of bank do vary

with different banks.

Table 3.43(C):Correlations (Responsibility of Managing a Bank Brand)

Bank

Name

Branch

Level

Regional

Level

National

Level

Top

Management

Level

Bank Name Pearson

Correlation 1 -0.109 0.078 0.164 -0.145

Sig. (2-

tailed) 0.535 0.656 0.347 0.405

Branch

Level

Pearson

Correlation 1 0.222 -0.056 -0.088

Sig. (2-

tailed) 0.199 0.748 0.615

Regional

Level

Pearson

Correlation 1 .489

** 0.213

Sig. (2-

tailed) 0.003 0.218

National

Level

Pearson

Correlation 1 .459

**

Sig. (2-

tailed) 0.006

Top

Management

Level

Pearson

Correlation 1

Sig. (2-

tailed)


The Questions was asked to Managers for finding their views on the

responsibility of managing a Bank Brand, lies at…

Looking at the above Table 3.43(C), we can conclude that Bank Brand does not

have any relationship with views on the responsibility of brand management.




e352

4% 6%

18%

32%

40%

As a Bankcustomer

I want good :Brand/Imageof the Bank

Strongly Disagree

Disagree

Neither Agree nor DisagreeAgree

Strongly Agree

And it remains same for all bank Brands, which is statically significant at 90%

confidence level.

We accept the null hypothesis, H0_53 and conclude that the views on the

responsibility of managing the brand of bank do not vary with different banks.

3.3.30: RQ_00: Does the brand image of the bank have any

effect on customers Bank Selection decision?

Null Hypothesis:

H0_00: Bank Selection Is Independent Of Its Brand Image.


H1_00: Bank Selection Is Dependent On Its Brand Image.

Table 3.44(A): Group Statistics (Importance of Brand Image of Bank) Bank

As a Bank customer I want good : Brand/Image


Percent

Cumulative

Percent

Valid Strongly

Disagree

40 3.8 3.8 3.8

Disagree 63 6 6 9.8

Neither

Agree nor

Disagree

190 18.1 18.1 27.9

Agree 335 31.9 31.9 59.8

Strongly

Agree

422 40.2 40.2 100

Total 1050 100 100


Chart 3.26: Importance of a Brand Image of a Bank




e353

Table 3.44(B): One-Sample Statistics (Importance of Brand Image of Bank) B


Brand/Image of the

Bank

1050 3.99 1.08 0.033

A. One-Sample ‘t’ Test:

Table 3.44(C): One-Sample Test (Importance of Brand Image of Bank)

Test Value = 3 t Df Sig.

(2-

tailed)

Mean

Difference

90% Confidence Interval

of the Difference

Lower Upper

As a Bank

Customer…

Brand/Image of

the Bank, is

Important to

Me

29.6 1049 0 0.987 0.93 1.04

B. ‘Z’ – Test:

As we want to test whether a „Bank Selection Is Independent of Its Brand

Image‟ or not (Q.13.c). Looking at the above Table 3.44(B) we have found the

avg. mean is at 3.99, which we can considered as 4, which means customer do

„Agree‟ with the statement that their bank selection decision is based on the

brand image of the bank. In order to statistically check the statistical validity of

this mean we will go for the Z test and T test for the average mean of above 3.0

(Neither Agree nor Disagree)

Ho: 3.0

H1: > 3.0

= (X- )/ X

Where, X= S/ n

X= 1.08/ 1050= 1.08/32.403= 0.03333

= (X- )/0.03333




e354

= (3.99-3)/0.03333

= 29.7029




the null hypothesis is rejected; reaching the same conclusion arrived using one

sample T-test in Table 3.44(C) with significance value of 0.000.

So we can conclude that the mean of customer rating is above 3.0 (above

neither agree nor disagree) for the statement of „Brand image of the bank is

important while bank selection‟. And thus we reject null hypothesis Ho_00 and

accept the alternate hypothesis H1_00: Bank selection is dependent on the

Brand Image of the Bank.




e355

References for the chapter 3:

1. Adapa Sujana (2008), “Discriminant Analysis of Adaptors and Non-Adopters of

Global Brands: Empirical Evidence from India and Malaysia”, ICFAI Journal of

Brand Management, Vol. V, No. 4, Dec., pp: 7-25.

2. Hair J F, Anderson R E, Tatham R L and Black W C (1998), Multivariate Data

Analysis, 5th Ed., Prentice-Hall, NJ.

3. Kothari C R (2004), Research Methodology – Methods & Techniques, New Age

International Pvt. Ltd., New Delhi.

4. Singh Kultar (2009), Quantitative Social Research Methods, Sage Publications, New

Delhi, India.

5. Malhotra Naresh K. (2004), Marketing Research, Pearson Education, Delhi.

6. Nargundkar Rajendra (2008), Marketing Research – Text and Cases, Tata McGraw-

Hill,Delhi.

7. Sukanya Ashokkumar and Shilpa Gopal (2009), “Diffusion of Innovation in Private

Labels in Food Products”, ICFAI Journal of Brand Management, Vol. VI, No.1,

March, pp: 35-56.

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