Alternative Banking Channels and Customers’ Satisfaction

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Alternative Banking Channels and Customers’ Satisfaction: An Empirical Study

of Public and Private Sector Banks

Article · January 2011

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International Journal of Business and Management Tomorrow Vol. 1 No. 1

www.ijbmt.com Page | 1

Alternative Banking Channels and Customers’

Satisfaction: An Empirical Study of Public and

Private Sector Banks

Vijay M. Kumbhar , M. A. SET, NET, GDC&A, DIT and M. Phil (Economics) and recently, he has submitted

PhD Thesis in Alternative Banking and Its Impact on Customers Satisfaction to Shivaji University, Kolhapur



ABSTRACT

The present empirical study focuses on identifying key factors that have influences

customers satisfaction in alternative banking service provided by public and private sector banks.

For the purpose of the present study primary data were collected using likert scale based

questionnaire. Result of this study shows that, there was significant relationship between age,

education and profession of the bank customers and customers‘ satisfaction in alternative

banking. There was significant relationship between service quality, band perception and

perceived value with overall customer satisfaction in alternative banking. Factor analysis test

indicates that, efficiency, security/assurance, cost effectiveness, problem handling,

responsiveness, fulfillment and accuracy were first factors, Perceived value, brand perception,

contact facilities, convenience, system availability and easy to use are second factor and

compensation is third factor. Overall result directs that, bankers should consider the facts and

enhance service quality of alternative banking services in order to increase customers‘

satisfaction and its further adoption also.

JEL: G20, G21

Keywords: Alternative Banking, Service Quality, Brand Perception, Perceived value,

Satisfaction

1.1 INTRODUCTION

Technology in the banks is presently catching up with a high level of development around

the world. The gaps between the Indian banks and their counterparts in the technologically

advanced countries are gradually narrowing down. The world has witnessed an information and

technological revolution of late. This revolution has touched every aspect of public life including

banking (Siam, 2006). Since two decades, due to an increasingly competitive, saturated and

dynamic business environment, retail banks in many countries have adopted customer-driven

philosophies to address the rapid and changing needs of their customers (Walker et al., 2008).

Technological advances have changed the world radically, altering the manner in which

individuals conduct their personal and business affairs. Over the past two decades in particular,

the banking industry has invested substantial resources in bringing ICT to customers. The

banking industry is undergoing through the significant technological changes; it has several

impacts on customer satisfaction and loyalty. ―It has revolutionised every industry including



banking in the world by rendering faster and cost effective delivery of products and services to

the customers. According to Chakrabarty, (2007) core banking solution enables banks to extend

the full benefits of ATM, tele-banking, mobile banking, internet banking, card banking and other

multiple delivery channels to all customers allowing banks to offer a multitude of customer-

centric services on a 24x7 basis from a single location, supporting retail as well as corporate

banking activities.

Now, Indian banks are investing heavily in the technologies such as branch automation

and computerization, core banking, tele-banking, mobile banking (M-banking), internet banking,

automated teller machine (ATMs), data warehousing etc. ICT innovations in the previous few

years have changed the landscape of banks in India (Mittal and Dhingra, 2007; Kour and Kour,

2011). Today public sector and private sector banks are offering online banking services. Various

alternative channels to provide easy and any where banking are properly thought of. The process

of bank computerization was started since 1985 in public sector banks in India. However, some

private sector banks have started computerization prior to the public sector banks in India. The

banks in India are using ICT not only to improve their own internal processes but also to increase

facilities and services to their customers.

A customer satisfaction is an ambiguous and abstract concept. Actual manifestation of

the state of satisfaction will vary from person to person, product to product and service to

service. The state of satisfaction depends on a number of factors which consolidate as

psychological, economic and physical factors. The quality of service is one of the major

determinants of the customer satisfaction, which can be enhanced by using ICT available to

survive. The banks in India are using Information Technology (IT) not only to improve their own

internal processes but also to increase facilities and services to their customers1. Particularly, in

the banking sector ICT is one of the most important tools, because it provides many suitable

alternative banking channels to the customers. It brings connivance, customer centricity, enhance

service quality and cost effectiveness in the banking services. Even now, customers are

evaluating their banks based on availability of high-tech services. Therefore, implementation of

ICT in the banking business continues to improve the banking service. Many researchers from

USA, UK, Finland, Malaysia, Taiwan, etc. have proved that the use of technology positively

affects the customers‘ satisfaction in banking industry. But some researches evidenced that,

1 RBI, Report on Trend and Progress of Banking in India 2008-09, pp 140



technology based banking service can‘t satisfy the each and every need of the customers‘ and

each type of customers‘. There are may be some possibilities of gaps between customers‘

expectations and actual service perception in ICT based banking service, which leads to

customer dissatisfaction. Hence, there is a need to assess the impact of alternative banking

services on customers‘ satisfaction in Indian context to study the level of satisfaction, problems

and areas of further improvements.

1.2 ALTERNATIVE BANKING

Customers are now looking for multiple delivery channels and flexible as well as

convenient working hours neither the clock nor the geographical locations are constraints.

Therefore, almost all Indian commercial banks are providing services through the various

alternative e-channels, it is called as ‘Alternative Banking’ (Shrotriya, 2007 and Kumbhar,

2009). There are various means of alternative banking i.e. Core banking Solution (CBS), ATM,

POS Terminals, Mobile Banking, Internet Banking, Credit Cards, Debit Cards, EFT, RTGS,

MICR clearing etc.

1.3 OBJECTIVES OF THE STUDY

Theoretically, alternative banking channels will enhance good performance of banking

services and increases the level of customer satisfaction by providing anytime, anywhere and

multi way banking services including varieties of services, convenience, speed, efficiency,

security and cost effectiveness. However, various annual reports of the Bank Ombudsman

Scheme of the RBI indicate that the use of ICT specifically after 2003 has created various

problems relating to the banking services. Therefore, this study is carried out to fulfill the

following adjectives:

1) To identify the factors of customers‘ satisfaction in alternative banking.

2) To make comparative analysis of customer satisfaction in public and private banks.

3) To identify the major factors affecting on the customers‘ satisfaction in alternative

banking.



1.4 REVIEW OF LITERATURE

Customer satisfaction is buzzword today, once here everyone using this customer‘s

satisfaction is affected by the importance placed by the customers on each of the attitudes of the

product/ service. Customer satisfaction measurement allows an organization to understand the

key drivers that create satisfaction or dissatisfaction; and what is really driving their satisfaction

during a service experience. Customer satisfaction is the state of mind that customers have about

a company when their expectations have been met or exceeded over the lifetime of the product or

service (Kevin Cacioppo, 1995 and Kumbhar, 2010). It is also feeling or attitude of a customer

towards a product or service after it has been used. According to Oliver (1980) satisfaction

appears to mediate changes between pre-exposure and post-exposure attitudinal components. It is

a major outcome of marketing activity whereby it serves as a link between the various stages of

consumer buying behavior (Jamal & Nasser, 2002). When customers pay money to buy a service

he has some minimum expectations from the transaction. These expectations from the purchase

have to be met substantially, if not entirely for the customer to become a loyal customer of the

service (Akbar and Parvez, 2009). These expectations are fulfilled of a promises- quality, fair

price, availability, after sale services, complaints handling process, information, and variety etc.

the customers are demanding high quality of services and low prices or charges. Better quality

for the same cost is the motto of the customers. Sometimes they are prepared to overlook

inconveniences also to avail better services at a low cost. Various empirical researches show that

there is significant and positive relationship in service quality and customer satisfaction. Berry

(1990) mentioned that there are ten 'Quality Values' which influence satisfaction behaviour i.e.

Quality, Value, Timeliness, Efficiency, Ease of Access, Environment, Inter-departmental

Teamwork, Front line Service Behavior, Commitment to the Customer and Innovation.

Rueangthanakiet Pairot, (2008) defined Customer‘s satisfaction as the company's ability to fulfill

the business, emotional, and psychological needs of its customers. However, customers have

different levels of satisfaction as they have different attitudes and experiences as perceived from

the company.

Increase in service quality of the banks can satisfy and develop attitudinal loyalty which

ultimately retains valued customers (Kumbhar, 2010). There is very strong relationship between

quality of service and customer satisfaction (Parasuraman et al, 1985). The higher level of

perceived service quality results in increased customer satisfaction. When perceived service



quality is less than expected service quality customer will be dissatisfied (Jain and Gupta, 2004

and Kumbhar 2011). Parasuraman, Zeithaml and Berry (1988) posited that if there is expected

quality of service and actual perceived performance is equal or near about equal there is

customers can be satisfy, while a negative discrepancy between perceptions and expectations a

‗performance-gap‘ as they call it causes dissatisfaction, a positive discrepancy leads to consumer

delight. The relationship between expectation, perceived service quality and customers

satisfaction have been investigated in a number of researches (Zeithaml, et al, 1996). An

expectation is minimum requirement of service quality by service providers to the meet

customers wants and needs. According to Parasuraman et al (1985, 1988) perceived service

quality is viewed as the degree and direction of discrepancy between customers' perceptions and

desires.

The branding is considered as the procedure of creating a brand image which keeps

consumers. It is what separates identical products from each other, or its competitors. Marketing

literature including NCSI and ACSI literature examined positive of the link between the

satisfaction and the brand image, the brand reputation (Wafa et al 2009) and indicates that, the

nature and amount of a consumer's experience with an evoked set of brands. Brand reputation

has significant impacts on customer satisfaction (Woodruff et al 1983). A consumer's beliefs

about these brands are derived from personal use experience, word-of-mouth

endorsements/criticisms, and/or the marketing efforts of companies. Perceived brand

performance which is above or below the norm, but within the indifference zone, leads to

confirmation. Positive or negative disconfirmation results when perceived brand performance

affects customer satisfaction (Woodruff et al 1983). Some of the marketing research shows that

the brand loyalty achieved through the satisfaction with brand performance. Brands that are high

in brand equity are organization powerful assets. They can lead to customer satisfaction and

customer loyalty. Wafa M‘Sallem et al (2009) and Sondoh et al,(2007) Some of the empirical

researches shows that there are two brand image benefits, i.e. appearance enhances and

functional have significant impacts on satisfaction and loyalty intention.

Perceived value also one of the important factors which influencing customers

satisfaction in service setting. Perceived value is compression between price or charges paid for

the services by the customer as sacrifice of the money and utility derived by service perception.

In this study we have assessed overall satisfaction also it can be say cumulative satisfaction. It is

overall perception and concluded remark of the customer regarding alternative banking channel



used by customers. The overall remark of the customer is based on his/her expectations about

various aspects of service quality and actual service he/she perceived by the particular bank.

1.5 HYPOTHESIS OF THE STUDY

Based on review of literature and discussions with experts followings hypothesis were

formulated to be testing;

Hypothesis-1:

H1(null) - Overall customers satisfaction in the alternative banking services is not

differ based demographic characteristics.

H1(alt) - Overall customers satisfaction in the alternative banking services is differ

based demographic characteristics.

Hypothesis-2:

H2 (null) - There is no significant relationship between service quality dimensions

and overall customer satisfaction in alternative banking

H2 (alt) - There is a significant relationship between service quality dimensions

and overall customer satisfaction in alternative banking

Hypothesis-3:

H3

(null) - Band perception and perceived value not have a significant relationship

with overall customer satisfaction in alternative banking.

H3

(alt) - Band perception and perceived value have a significant relationship with

overall customer satisfaction in alternative banking.

Hypothesis 4

H4 (null) – All service quality dimensions under study was similarly influencing

customers‘ satisfaction in alternative banking.

H4 (alt) – All service quality dimensions under study was similarly influencing

customers‘ satisfaction in alternative banking.



1.6 RESEARCH METHODOLOGY

Present study is based on primary data sources; collected through survey of 150

respondents. Primary data was collected through survey and short interviews of bank customers

(Satara City, Maharashtra State) who are using alternative banking channels. All respondents were

selected using convenience sampling method due to lack of proper information and availability of

time.

Approximately 25% of commercial banks from public and private sector banks which

have a maximum level of branch automation and providing most of the alternative banking

services in the Satara City. For the selection of banks we have conducted primary investigation

to investigate the availability of the alternative banking services. We found that State Bank of

India, Bank of Baroda, Corporation Banks and IDBI Bank Ltd. from public sector and HDFC

Bank Ltd. and Axis Bank Ltd. from private sector banks are providing almost of alternative

banking services. We have selected approximate 25 to 35 respondents from each selected banks

and total 190 predictive samples for in-depth and qualitative investigation. We have selected the

sample from employed, professional persons, businessmen, students and retired persons. The

previous research (Ndubisi & Sinti, 2006; Thomas Meyer, 2008; Mattila et al, 2003; Sanmugam,

2004; Mathews and Slocum, Jr. 1969; Plummer, 1971; Vasya and Patrik, 2006; Noor and Ali,

2009) proved that near about 75 to 85 per cent users of electronic banking service were

professional, graduates or highly literates, holding an executive post, young employed or self-

employed, having good income, resides in urban areas, having knowledge of computer and

proficient in the English language.

Survey questionnaire was developed through review of literature and discussions with

experts and all responses were recorded based on five point likert scale form 1= sternly disagree to

5 = strongly agree. Primary questionnaire was tested using Cronbach‘s alpha reliability test and

only those dimensions were used which having alpha value 0.700 or more. Collected data was

analyzed through SPSS 18.0, particularly; descriptive statistics, Multiple correlation, Kruskal-

Wallis test, Mann-Whitney Test and Principal Component Analysis (PCA) was performed

according to the requirement of the study.



1.7 RESULTS

1.7.1 DEMOGRAPHIC OF THE RESPONDENTS

Table-1 indicates the descriptive analysis for demographic information indicated that

among the analyzed samples (N=190),consisting 17.4% of SBI, 14.7% of BOB, 13.2% of Corp

Bank, 18.4% of IDBI Bank, 15.8% of Axis Bank and 20.5% of HDFC Bank (63.7% of Public

Sector and 36.3% of private sector Banks). Out of 190 respondents 82.1% of the respondents

were male, 17.9 % were female. In terms of age group, 20% were below 25 years, 34.7% of 25

to 35 years, 35.8% were 36 to 50 years and 9.5% were 51 to 60 years old. There were no

respondent above 60 years however; some retired persons from military and army were covered

under study as samples. Educational status of the respondents indicates that 4.2% of respondents

were below HSC, 5.3% of HSC, 49.5% of graduate and 41.1% of post graduates. There were

31.6% of employees and 36.3% of businessmen as a core respondent who were using most of

alternative channels. However, 13.7% of professional (doctor, engineers, charted accountants,

investment consultants, insurance agents etc.), 14.2% of students and 4.2% of retired persons

also covered in this study. Income profile of the respondents shows that there were 20.5% of

below Rs. 1lakh, 16.3% of 1to 3 lakh, 36.8% of 3 to 8 lakh, 14.2% of 8 to 15 lakh, 4.7% of 15 to

25 lakh, 2.1% of above 25 lakh and 5.3% of dependents.

Table 1: Demographic Profile of the Respondents

Items Frequency Percent Valid Percent Cumulative Percent

SBI 33 17.4 17.4 17.4

BOB 28 14.7 14.7 32.1

COPB 25 13.2 13.2 45.3

IDBI 35 18.4 18.4 63.7

Axis 30 15.8 15.8 79.5

HDFC 39 20.5 20.5 100

Total 190 100 100

Female 34 17.9 17.9 17.9

Male 156 82.1 82.1 100

Total 190 100 100

Below 25 38 20 20 20

25-35 66 34.7 34.7 54.7

36-50 68 35.8 35.8 90.5

51-60 18 9.5 9.5 100

Total 190 100 100

<HSC 8 4.2 4.2 4.2

HSC 10 5.3 5.3 9.5

Graduate 94 49.5 49.5 58.9



Post Graduate 78 41.1 41.1 100.0

Total 190 100.0 100.0

Employee 60 31.6 31.6 31.6

Businessman 69 36.3 36.3 67.9

Retired 8 4.2 4.2 72.1

Student 27 14.2 14.2 86.3

Professional 26 13.7 13.7 100

Total 190 100 100

<1 Lakh 39 20.5 20.5 20.5

1 to 3 Lakh 31 16.3 16.3 36.8

3 to 8 Lakh 70 36.8 36.8 73.7

8 to 15 Lakh 27 14.2 14.2 87.9

15 to 25 Lakh 9 4.7 4.7 92.6

>25 Lakh 4 2.1 2.1 94.7

Dependents 10 5.3 5.3 100.0

Total 190 100.0 100.0

Source: Survey

1.7.2 COMPARATIVE OVERALL CUSTOMER SATISFACTION

Various previous researches proved that there was variation in customer satisfaction in

public and private sector banks. It was realized that there was a significant difference in customer

satisfaction as concern to bank type Table 2 indicates that most of respondents of private sector

banks rated the level of satisfied (60.00%) and strongly satisfied (17.78%) for front office

services and Satisfied (64.44%) strongly satisfied (2.22%) for core banking services. The Table

further indicates the service provided through currency counting machine which is most

satisfactory in private sector banks because 51.43% customers rated that it was satisfactory and

10.48% are rated that it is strongly satisfactory services in public sector banks. EFT service and

MICR service of public sector banks was very satisfied because 51.43% and 10.48% EFT service

user and 56.73 and 5.77% MICR service users rated these services satisfied and strongly satisfied

respectively.



Table 2: Comparative Overall Satisfaction in Alternative Banking (%)

Public Banks Private Banks All Banks

Front Office

Services

Strongly Dissatisfied 0.00 0.00 0.00

Dissatisfied 4.76 0.00 2.38

Neutral 22.86 22.22 22.54

Satisfied 54.29 60.00 57.14

Strongly Satisfied 18.10 17.78 17.94

Numbers of Respondents 120 70 190

Core

Banking

Service



Neutral 30.77 28.89 29.83

Satisfied 50.00 64.44 57.22



Service of

CC Machine



Neutral 28.57 28.89 28.73

Satisfied 51.43 57.78 54.60



EFT Service



Neutral 32.69 33.33 33.01

Satisfied 56.73 57.78 57.25



MICR

Service



Neutral 30.61 32.56 31.58

Satisfied 58.16 60.47 59.31



Note: Percentage of ranting calculated based on customers of public and private sector banks

separately

Table 3 reveals that public sector banks‘ Credit card and ATM service are providing

satisfactory credit card and ATM service, because 55.26% and 5.26% of customers belongs to

public sector a banks rated that credit card was satisfactory and strongly satisfactory. The

68.93% and 10.68% of customers of public sector banks rated as ATM was satisfactory and

strongly satisfactory respectively. However, satisfaction in internet banking services was

approximately same in private and public sector banks. Near about 50% of customers were rated



that internet banking service was satisfactory in both. While, 50% customers of private sector

rated that mobile banking service is satisfactory in private sector banks. However, only 178%

customers of public sector banks are satisfied in mobile banking services.

Table 3: Comparative Overall Satisfaction in Alternative Banking (%)

Pub. Banks Pvt. Banks All Banks

Credit Card

Services



Neutral 34.21 25.00 29.60

Satisfied 55.26 37.50 46.38



ATM Service



Neutral 10.68 13.64 12.15

Agree 68.93 72.73 70.82

Strongly Agree 10.68 9.09 9.88


Internet

Banking Service



Neutral 50.00 50.00 50

Satisfied 45.00 50.00 47.5



Mobile Banking

Service



Neutral 83 50 66.67

Satisfied 17 50 33. 33

Strongly Satisfied 0 0 0


Note: Percentage of ranting calculated based on customers of public and private sector

banks separately

1.7.3 Hypotheses testing

There are various tests available for hypotheses testing; however, we have used Kruskal-

Wallis One-Way Analysis of Variance Test, spearman’s rho non-parametric correlation and

Mann-Whitney U Test as non-parametric tests, because the collected data in the present study is

qualitative data and normally distributed. Hence, it need to analysis using non-parametric tests.

The Kruskal-Wallis test is a one-way analysis of variance by ranks. It tests the null hypothesis

that multiple independent samples come from the same population. Unlike standard ANOVA, it



does not assume normality, and it can be used to test ordinal variables (SPSS 19.0 User manual).

It is a nonparametric alternative to one-way ANOVA is given by the Kruskal-Wallis test (Landau

and Everitt, 2004, pp 156-157) and it is a very powerful test for one-way analysis of variance for

measurements by rank of ‗k’ independence samples. H0 tested is that ‗k’ samples come from the

same population (Majumdar, 2010, pp 424-426; Hanagal, 2009, pp 10.10-10.12). In this method

the scores of ‗k’ samples are first considered as belonging to the same series. Rank of each score

is then ascertained from K combined sample series. The smallest score is assigned rank 1st and

the largest n, where, n = n1 + n2 + nk. For each of the samples, sum of rank are worked out. If

H0 is true, that is, ‗k’ samples are actually drawn from the same population, and then Kruskal-

Wallis statistics, designated as H, follows a distribution similar to Chi-Square with df = k-1

proved that the size of all k samples is not too small.

Hypothesis-1:

Most of previous researches mentioned that gender, age, education; profession and level

of income affect customers‘ satisfaction in banking services. Therefore, we have tested this

hypothesis to know ―what is actual impact of the demographics and customer satisfaction in

alternative banking services?‖ The Kruskal-Wallis Chi Square tests (non parametric test) were

performed to overall satisfaction in the alternative banking services was differing or not by

demographic characteristics group. In order to test this hypothesis The Kruskal-Wallis tests for

several independent (gender, age, education, income level and profession) and one dependent

variable (overall satisfaction) was performed (Nahmens and Ikuma, 2009 pp 586 and Khalid et

al, 2000). According to Kruskal-Wallis large values of H (Chi-Squire) lead to rejection of the

null hypothesis and same or less than table value lead to accept null hypotheses (Kruskal-Wallis,

1952). Chi-Square values of Gender (X2 =0.217 , df =1, sig.= .462) and Level of Income (X

2 =

7.154 , df =6, sig.= .307) Age (.057) are found significant. It leads to accept null hypotheses

(H1a and H

1e) and reject alternative hypothesis. However, Chi-Squire values of Age (X

2 =13.313

, df =4, sig.= .010), Educational level (X2 =12.241 , df =4, sig.= .016) and Profession (X

2

=14.165 , df =4, sig.= .015) are found insignificant and leads to reject null hypothesis (H1b, H

1c,

H1d) and accept alternative hypothesis. Descriptive statistics also shows that there was

significant relationship between age, education and profession while level of customer

satisfaction of male and female, as well as respondents of belongs to different income groups is

almost same (Table 4)



Table 4: Hypothesis Test Summary Kruskal Wallis H Test

(Demographics and Overall Customer Satisfaction)

Null Hypothesis N Calcu.

Value

X2

Table

Value

X2

df Sig. Decision

H1a Gender 190 0.217 3.841 1 .462 Retain null hypothesis.

H1b Age 190 13.313 9.488 4 .010 Reject null hypothesis.

H1c Education 190 12.241 9.488 4 .016 Reject null hypothesis.

H1d Profession 190 14.165 9.488 4 .015 Reject null hypothesis.

H1e Level of Income 190 7.154 12.592 6 .307 Retain null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Hypothesis-2:

The second hypothesis of this study has been tested using correlation test. Here

spearman’s rho non-parametric correlation test was performed to understand correlation

between each of service quality dimensions and overall customer satisfaction in alternative

banking. As per SPSS 19.0 user manual multiple correlation test is useful to assess relation

between multiple independent variable and one dependent variable. Therefore, we have

performed spearman‘s rho non-parametric correlation test and result shows that there was a

significant relationship between all dimensions and overall customer satisfaction, it leads to

accept null hypothesis. Therefore we have rejected null hypothesis (H2) and accepted alternative

hypothesis. However, Table 5 indicates all dimensions are significantly correlated to overall

customer satisfaction except responsiveness. Therefore, we have accepted alternative hypothesis

and rejected null hypothesis.

Table 5: Correlation Between Service Quality Dimensions and

Customer Satisfaction (Spearman’s Correlation)

Overall Satisfaction

Overall Satisfaction r 1.000

Sig. (2-tailed) .

System Availability r .519**

Sig. (2-tailed) .000

E-Fulfilment r .547**


Accuracy r .573**


Efficiency r .560**


Security/Assurance r .594**




Hypothesis-3:

A brand is an important factor in the selection of the bank as service provider either from

private or public sector. Mostly well-known banks were selected by the customers because it was

assumed that; good brand name and perception as per brand name can satisfy customer‘s wants.

Hence, bank should provide better service as per their reputation. If customers getting good

perception according to the brand name customer will happy with that brand otherwise they will

switch to other bank. The concept of value derives from an economic framework and reflects the

utility and disutility encountered within any transaction. It provides a relationship between the

benefits received in the encounter and the overall price paid, including non-monetary

expenditure (Woodruff, 1983, Zeithaml, 1988) cited by Wilkins ,2005). Therefore, we have

tested this hypothesis in using spearman‘s rho non-parametric correlation test. Table 6 shows

that, there is significant relationship between band perception (r = .652** at .050 level) and

perceived value (r = .646** at .050 level) with overall customer satisfaction. Therefore we have

rejected null hypothesis and accepted alternative hypothesis based on this study.

Responsiveness r .208**


Easy to Use r .585**


Convenience r .598**


Cost Effectiveness r .541**


Problem Handling r .646**


Compensation r .324**


Contact r .626**


*Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).



Table 6: Correlation Between Brand Perception, Perceived Value and Customer

Satisfaction

Overall Satisfaction

Overall Satisfaction r 1

Sig. (2-tailed) .

Brand Perception r .652**


Perceived Value r .646**


Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).

1.7.4 PRINCIPAL COMPONENT ANALYSIS

Principal component analysis method was used to identify (Test hypothesis 4) the major

factors affecting on customers‘ satisfaction in alternative baking services. Factor analysis is one

of the superior methods to understand most useful factors in the model (Widaman, 1993).

Before, conducting factor analysis, we have performed the Kaiser-Meyer-Olkin (K-M-O) and

Bartlett‘s sphericity test to understand adequacy of the data for factor analysis. High values of

the Kaiser-Meyer-Olkin measure (close to 1.0) generally indicate that a factor analysis may be

useful with data (Marshall et al, 2007; Barco et al 2007). If the value is less than 0.50, the results

of the factor analysis probably won't be very useful. Bartlett's test of sphericity tests the null

hypothesis that correlation matrix is an identity matrix, which would indicate that variables are

unrelated and therefore, unsuitable for structure detection. Small values (less than 0.05) of the

significance level indicate that R-Matrix is not an identity matrix and a factor analysis may be

useful with data. Table 7 indicates that, in the present test the Kaiser-Meyer-Olkin (KMO)

measure was 0.819. Bartlett‘s sphericity test also found highly significant; Chi-Square =

1170.841, df = 91 with a significance of 0.000 it provide support for validity of the factor

analysis of the data set and indicates that, factor analysis is appropriate.

Table 7: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure .819

Bartlett's Test of Sphericity

Approx. Chi-Square 1170.841

df 91

Sig. Bartlett .000



Initial communalities2 are, estimates of the variance in each variable accounted for by all

components or factors. For principal components extraction, this is always equal to 1.0 for

correlation analyses. Extraction communalities are, estimates of the variance in each variable

accounted for by the components. The communalities (Table 8) labeled as Initial are before

extraction communalities and labeled as extraction are after extraction communalities. The all

communalities were high (above .400), which indicates that the extracted components represent

the variables well (Table 8).

Table 8: Communalities

Initial Extraction

System Availability 1.000 .648

Fulfilment 1.000 .755

Accuracy 1.000 .517

Efficiency 1.000 .805

Security/Assurance 1.000 .764

Responsiveness 1.000 .641

Easy to Use 1.000 .486

Convenience 1.000 .497

Cost Effectiveness 1.000 .733

Problem Handling 1.000 .743

Compensation 1.000 .741

Contact Facilities 1.000 .621

Brand Perception 1.000 .682

Perceived Value 1.000 .724

Extraction Method: Principal Component Analysis.

Table 9 reveals that, the eigenvalues3 associated with each linear component before

extraction and after extraction. The eigenvalues associated with each factor represent the

variance explained by the particular linear component (Khan, 2006). First part of the table

labelled as ‗Initial Eigenvalues‘ indicates that, first factor accounting 40.32 % of variance in this

model. Second part of the table labelled as ‗Extraction Sums of Squared Loadings‘ indicating

same variance. However, third part labelled as ‗Rotation Sums of Squared Loadings‘ indicating

that first factor accounting 32.37% of variance. It is pretty good figure because total variance is

66.83%. It means, we lost only 35% approx. of the information content in our study. Rotation

has the effect of optimising the factor structure and one consequence for these data is that the

2 Communality is the squared multiple correlation for the variable as dependent using the factors as predictors. The

communality measures the percent of variance in a given variable explained by all the factors jointly and may be interpreted as

the reliability of the indicator 3 The eigenvalue for a given factor measures the variance in all the variables which is accounted for by that factor. The ratio of

eigenvalues is the ratio of explanatory importance of the factors with respect to the variables. If a factor has a low eigenvalue,

then it is contributing little to the explanation of variances in the variables and may be ignored as redundant with more important

factors.



relative importance of the three factors were equalized (Field, 2005). Figure 1 (The components

as the X axis and the corresponding eigenvalues as the Y axis) indicates visible display of the

factor loading.

Rotation serves to make the output more understandable and is usually necessary to

facilitate the interpretation of factors, therefore we have performed rotted matrix (Shapiro, 2002).

The rotated component matrix (regression scores) table indicate factor loading. This is a matrix

of the factor loading for each variable onto each factor. Table 6.35 indicates that, most of

variables loaded highly onto the first and second factors and only one variable loading onto third

Table 9: Total Variance Explained

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Varia

nce

Cumul

ative % Total

% of

Variance

Cumul

ative

%

Total % of

Variance

Cumula

tive %

1 5.646 40.328 40.328 5.646 40.328 40.328 4.533 32.379 32.379

2 2.409 17.209 57.537 2.409 17.209 57.537 3.451 24.647 57.026

3 1.301 9.294 66.831 1.301 9.294 66.831 1.373 9.805 66.831

4 .870 6.217 73.048

Figure 1: Scree Plot

5 .653 4.663 77.711

6 .591 4.221 81.932

7 .511 3.649 85.581

8 .461 3.291 88.873

9 .406 2.899 91.772

10 .370 2.641 94.413

11 .287 2.048 96.461

12 .227 1.621 98.082

13 .155 1.107 99.189

14 .113 .811 100.00

Extraction Method: Principal Component Analysis.



factor. However, fulfillment (.735 in the first factor and .408 on the second factor) and system

availability (.471 in the first factor and .630 on the second factor) were loading on both factors.

Table 10: Rotated Component Matrixa

Factor 1 Factor 2 Factor 3

Efficiency .868

Security/Assurance .853

Cost Effectiveness .825

Problem Handling .773

Responsiveness .735

Fulfillment .735 .408

Accuracy .626

Perceived Value .827

Brand Perception .806

Contact Facilities .683

Convenience .682

System Availability .471 .630

Easy to Use .606

Compensation .808

Eigenvalues 4.533 3.451 1.373

Variance 32.379 24.647 9.805

Cumulative Variance 32.375 57.026 66.831 Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 5 iterations.

Table 10 indicates that, Efficiency, Security/Assurance, Cost Effectiveness, Problem Handling,

Responsiveness, Fulfillment and Accuracy are first factors and accounting 32.37% of variance in

the model. Perceived Value, Brand Perception, Contact Facilities, Convenience, System

Availability and Easy to Use are second factor and accounting 24.64% of variance in the model.

Compensation is third factor and accounting 9.80% of variance. All these three factors were

accounting total 66.83% of variance.

1.8 Conclusion and Implications

The present study evident that, there was significant relationship between age, education

and profession while level of customer satisfaction of male and female, as well as respondents of

belongs to different income groups is almost same. The correlation test shows that there was a

significant relationship between all dimensions and overall customer satisfaction in alternative

banking. There was also significant relationship between band perception and perceived value



with overall customer satisfaction in alternative banking. It indicates that, bankers should

concentrate their efforts to enhance service quality, brand perception and perceived value in

alternative banking to increase the level of customers‘ satisfaction. Factor analysis test indicates

that, efficiency, security/assurance, cost effectiveness, problem handling, responsiveness,

fulfillment and accuracy were first factors and accounting 32.37% of variance. Perceived value,

brand perception, contact facilities, convenience, system availability and easy to use are second

factor and accounting 24.64% of variance. Compensation is third factor and accounting 9.80% of

variance. Therefore, bankers should consider the facts and enhance service quality of alternative

banking services in order to increase customers‘ satisfaction and its further adoption also.

Authors contact:-

Vijay M. Kumbhar

( M. A. SET, NET, GDC&A, DIT and M. Phil (Economics)



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Alternative Banking Channels and Customers’ Satisfaction

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