Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Assessment of Customers’ Perception for Service
Quality of Private Sector Banks of Uttar Pradesh-India
Anoop Kumar Singh*
Private sector banks have become a significant constituent of Indian Financial System and
since the inception of financial sector reforms, they have been progressively increased their
share in total banking business in India. Although the share of these banks in total banking
business of Scheduled Commercial Banks is around 19 percent but they are now giving
tough competition to public sector banks. This research paper aims at performance
assessment of private sector banks of the urban areas of Uttar Pradesh, the largest state of
India in terms of population. For analyzing the client’s perception and expectation towards
service quality of private sector bank, SERVPERF model has been used. The assessment
criteria adopted in this study were the items included in the SERVPERF model and the
relative importance of the dimensions of tangibility, reliability, responsiveness, assurance,
and empathy were examined along with other preferences. Researcher found significant
positive interrelationships among the constructs of the proposed framework. In this study,
five-common factor measurement model was found to be valid and reliable to be used in
determining performance of the private sector banks. The findings of this study will help the
private sector banks in understanding their present status as well as in designing marketing
strategies according to their clients’ preferences. It would be a piece of research work that
may suggest the apex institutions to encourage banks to improve their performance.
Keywords: Customer service, SERVPERF, Perception, Expectations, Private sector bank
Field (Track) of the Research: Banking
1. Introduction:
For accelerating economic growth rate as well as overall socio-economic development in a
developing economy like India, a Sound financial system is imperative. In India, banking
sector has undergone remarkable changes and has attained new horizon of services with the
introduction of many new financial products into the Indian market. Government has also
initiated various measures of reform to stimulate the Indian banking sector and to cope up
with future financial challenges „To survive in today‟s competitive business environment,
banks need to focus on building and maintaining client relationships‟ (Rootman et al., 2008;
Abdullah & Ramay, 2012). Currently, India has 96 scheduled commercial banks (SCBs)
including 27 public sector banks (Government of India Undertaking), 31 private banks (not
having government stake but may be publicly listed and traded on stock exchanges) and 38
foreign banks. They have a combined network of over 53,000 branches and 17,000 ATMs. As
on 31st March, 2013, there are total 13167 branches of scheduled commercial banks in Uttar
Pradesh comprising of 6008, 2711, 2429 and 2019 branches in rural, semi urban, urban and
metropolitan cities respectively. The major private sector banks in India as well as in Uttar
Pradesh are ICICI Bank, HDFC Bank, Axis Bank, IndusInd Bank, Bank of Rajasthan, ING
Vysya Bank, Bharat Overseas Bank, Catholic Syrian Bank, Dhanalakshmi Bank, South Indian
Bank, City Union Bank, Federal Bank, Jammu & Kashmir Bank, Karnataka Bank, Karur Vysya
__________________________________________________________________________
Anoop Kumar Singh, *Department of Applied Economics, University of Lucknow, Lucknow. Mailing Address: 57C, Badshah Bagh, Lucknow University Campus, Lucknow- 226007, (U. P.) –INDIA
E-Mail: singhaklu@gmail.com Mobile: 09450931858
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Bank, Kotak Mahindra Bank, Lakshmi Vilas Bank, Nainital Bank, Ratnakar Bank, Saraswat
Bank, Tamilnadu Mercantile Bank, YES Bank etc. Private sector banks are playing a
significant role in the upliftment of economic realm of India by providing a sound financial
infrastructure in the services sector. A rapid increase in the number of banks operating in
Uttar Pradesh in the last few years has made the banking sector enter into competitive market
in which every bank is trying to get maximum market share. As far as number of offices of
private banks in Uttar Pradesh is concerned, it was 664 as on 31 st March, 2013 and 746 as on
31st march 2013. In Uttar Pradesh, as on 31st March, 2013, the deposits of private sector
banks were Rs. 425.40 billion (8.25 %) as against total deposits of Rs. 5150.15 billion for all
Schedule Commercial Banks. At the same time, credit sanctioned by private banks in Uttar
Pradesh was Rs. 177.79 billion which stood at 7.9% of the total credit extended by all
scheduled commercial banks in the state.
The current study illustrates that service quality in private sector banks is based on
meeting/exceeding expectations. To date, The SERVPERF technique is the most popular and
tested general measure of service quality particularly in banks. This instrument has been
widely adopted by both managers (Parasuraman, Zeithaml and Berry, 1991) and
academicians (Babakus and Boller, 1992; Cronin and Taylor, 1992; Carman, 1990; Crompton
and MacKay, 1989) to evaluate client‟s perceptions of service quality for banking services.
Researcher interrogates customers to compare their perceptions of the service process and
outcome against what they expected to receive from private sector banks. The present study
is focused upon an empirical assessment of the degree of effect of various service quality
dimensions on perceived satisfaction of client of private sector banks in Uttar Pradesh. Indian
bankers consider excellent service quality to clients as a key to success and sustainable
development, the findings from the study would provide them with valuable insights in ways of
improving service quality so that customers of private sector banks can be encouraged to
show positive behavioural outcomes. The main objectives of the present study can be
summarised as under:
1. To examine the service quality of private banks in terms of five major dimensions
2. To identify the gap between customer expectation and perception with respect to banks‟
performance
3. To suggest the important measures that need to be implemented by banks to ensure
superior quality of service.
2. Literature Review:
The present study starts with the theoretical literature review of the existing knowledge about
private sector banking system in India. For sustainable and consistent growth of an
organisation, service quality has become a significant approach. This research paper contains
an overview of literature on service quality in Indian banking sector as well as other countries
also. The following are some of the studies and their relevance to the researcher‟s area of
interest. (Jain and Jain, 2006) states that „during the economic reform period Banking Industry
has undergone drastic changes due to liberalization and globalization measures undertaken
by Indian Government‟. The study based on responses received from 200 customers of
HDFC bank, ICICI bank and some other private and nationalized banks in Varanasi revealed
that „service quality has been increasingly recognized as a critical factor in the success of any
business and the banking industry in this case in not exceptional‟ (Parasuraman et al., 1988 ;
Hossain & Leo, 2009). Service quality has been widely used to evaluate the performance of
banking services (Cowling & Newman, 1995). The banks understand that customers will be
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
loyal if they provide greater value (quality services) than their competitors (Dawes & Swailes,
1999). (Sharma S et al, 2007) found that „service quality is associated with satisfaction and
there was significant difference between quality of services provided by banks in smaller cities
and big cities. Smaller cities are far behind big cities in this regard‟. (Padhy & Swar, 2009)
concluded that „foreign banks are very close to expectations of customers followed by ICICI
and AXIS Bank on the basis of the study made on impact of technology on perceived service
quality in public, private and foreign banks in Orissa. It was found that the Service quality in
public sector banks was found to be very low‟. (Sachin Mittal & Rajnish Jain, 2010) indicated
in their study that „the gaps between customer‟s expectations and perception with respect to
IT based banking services are wider. It was suggested to improve the IT based services for
enhancing customer satisfaction.‟ (Maya Basant Lohani & Pooja Bhatia, 2012) found that
„there exists a small perceptual difference regarding overall service quality with the respective
banks. The respondents of both type of banks mostly concentrate on the staffs of the banks
for improving customer satisfaction while the bank have more concentration on the tangible
factor like a computerization, physical facilities, etc. to attract the customers. (A.Ananth,
R.Ramesh,& Dr.B.Prabaharan, 2011) concluded that „ empathy is having more gap between
customer expectation and perception of service quality. They suggest that „bank has to
reduce this gap giving individual personal attention to understand the customer specific
needs. The bank management should concentrate on proper maintenance of ATM‟.(Sarika
Bindal and Dr. R. K. Rastogi, 2014) concluded that The Reliability dimension of service quality
is better as compared to empathy and tangibility. Customers of the bank hesitate to rely on
the bank.
3. Research Methodology:
The present study is based on cross-sectional research design wherein empirical data have
been collected from different types of bank customers. The accumulated data analysis is
made to provide insights to answering research questions. The survey questionnaire was
constructed using the SERVPERF model containing five dimensions namely tangibility,
reliability, responsiveness, assurance, and empathy for gathering information from the
respondents. Based on five dimensions as discussed above, total 22 attributes in total are
used to assess the customers opinion about the banks‟ services The current study in Uttar
Pradesh shows that the sub scale „reliability‟ deals primarily with the outcome of service
delivery, whilst the other four dimensions (tangibility, responsiveness, assurance, and
empathy) concern the process of service delivery in private sector banks. The respondents
were asked to rate their expectations and perceptions of service offered by the respective
banks. For selecting the targeted respondents convenience sampling i.e. non-random
sampling was used. 400 account holders having their Savings Accounts in any of the five
private sector banks in four districts of Uttar Pradesh namely Lucknow, Agra, Varanasi and
Ghaziabad has been selected and interviewed. The banks selected in these cities for the
purpose of enquiry were ICICI, Indus Ind, HDFC, AXIS, and ING Vysya. Among the total of
400 respondents, 20.25% were between 21-30 yrs, 26.75% were between 31-40 yrs, 32%
were between 41-50 yrs, 12.5% were between 51-60 yrs, and only 8.5% respondents were in
the age group of 61yrs and above. In terms of sex category 74% were male and 26% were
female. Within the entire group 8.5% were intermediate, 48% were graduates, 31% were post
graduates, and remaining 12.5% were professionals. As per the occupational categories of
the respondents, 11.5% were in government job, 35.5% were private job holders, 39% were
self-employed, 5.25% were retired from Government or semi-Government Departments and
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
rest 8.75% were students. The basic objective of selecting the respondents from among the
different strata of the society was to ensure complete representation of the population units
having different kinds of expectations and needs. A statistical package for social sciences
(SPSS) version 20 was used to analyze the data compiled from the administered
questionnaires. Firstly, based on the variation between expectations and perceptions
regarding different parameters, an overall quality score was computed. Thereafter paired ttest was used to assess the significant difference between the perception and expectation.
Secondly, the validity of SERVPERF Model has been tested by using confirmatory factor
analysis.
4. Findings/Discussion:
Having collected the data through administering SERVQUAL questionnaire, the data have
been analyzed by applying various statistical tools. Following are the various aspects of the
analysis and results obtained thereby.
4.1. Gap analysis:
The ultimate measure of quality is whether or not the product or service lives up to
expectations of the clients. The most widely used and tested service quality instrument has
been accepted as SERVPERF, based on the service quality „gap model‟ (Parasuraman et al.,
1988, 1991, 1993, 1994). The instrument represents a multi-item scale for measuring client
expectations and perceptions of service quality in private sector banks. It consists of 22
parallel expectation (E) and perception (P) statements in the five service quality dimensions.
In order to obtain views for the statement, clients are asked to rate (on a five-point Likert scale
from “Strongly Disagree” to Strongly Agree”) what kind of service they expected from the
banks and how they perceived the banking service.
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Table-1
S.No.
Statements
Perception
Mean (S.D)
Expectation
Mean (S.D)
Q (P-E)
Gap
4.18 (.731)
3.75(.694)
4.19 (.650)
3.89 (.924)
-.42
-.36
-.64
-.23
4.18 (.570)
4.27 (.612)
4.02 (.706)
3.97 (.618)
4.13 (.792)
-.64
-.44
-.36
-.38
-.41
4.13 (.687)
4.26 (.609)
4.19 (.680)
3.85 (.886)
-.59
-.58
-.62
-.29
3.94 (0.95)
4.42 (.624)
4.26 (.670)
4.56 (.759)
-.47
-.46
-.72
-.44
4.06 (.826)
4.45 (.826)
3.99 (.814)
4.13 (.783)
4.12(.679)
90.94
4.13
-.28
-.39
-.35
-.50
-.55
-10.12
-.46
Tangibility
1.
2.
3.
4.
Modern-looking equipment
Physical facilities
Neat appearing
Visually appealing
3.76 (.636)
3.39 (.558)
3.55 (.637)
3.66 (.711)
Reliability
5.
6
7.
8.
9.
Promise to do something
Sincere interest in solving it
Perform the service
Provide the service
Insist on error free records
3.54 (.757)
3.83 (.779)
3.66(.794)
3.59 (.895)
3.72 (.812)
Responsiveness
10.
11.
12.
13.
Tell customers exactly
Prompt service to customers
Willing to help customers
Respond to customers‟ request
3.54 (.636)
3.68 (.754)
3.57(.753)
3.56 (.853)
Assurance
14.
15.
16.
17.
Confidence in customers
Feel safe in transactions
Courteous with customers
Answer customers‟ questions
3.47 (.840)
3.96 (.752)
3.54 (.589)
4.12 (.716)
Empathy
18.
Individual attention
19.
Operating hours convenient
20.
Personal attention
21.
Best interest at heart
22.
Understand specific needs
SERVQUAL totals
SERVQUAL average
3.78 (.653)
4.06 (.782)
3.64(.708)
3.63 (.682)
3.57 (.680)
80.82
3.67
The mean scores from the sample are illustrated in the above tables. For each statement the
mean Expectation (E) and Perception (P) values, along with a service quality value from the
formula Q = P – E, are presented, (Parasuraman et al., 1988). The gap on all the items is
negative; this refers to perceptions of the customers for service quality of banks falls short
against initial clients‟ expectations, and the presence of service quality gaps. Mean scores
greater than three signifies a tendency for respondents to agree with a particular statement,
whereas means of less than three indicate disagreement. The service quality gaps (P-E) are
demonstrated in the third column of table. As each item has a negative value, clients‟
perceptions of the bank‟s service are falling short of their expectations.
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Gap analysis of perception and expectation
5
4.5
4
Mean
3.5
3
Perception
2.5
Expectation
2
1.5
1
0.5
0
1 2 3 4 5 6 7 8 9 10111213141516171819202122
Figure 1: Gap analysis of perception and expectation
4.2. Paired sample t-test statistics:
In order to test whether service quality gaps are statistically significant or not, Paired sample ttests were introduced.
Table-2: Paired sample t-test statistics
S.No.
Statements
t-test
p-value
1.
2.
3.
4.
Modern-looking equipment
Physical facilities
Neat appearing
Visually appealing
3.454
3.798
6.032
1.763
.001
.000
.000
.023
5.
6
7.
8.
9.
Promise to do something
Sincere interest in solving it
Perform the service
Provide the service
Insist on error free records
5.305
4.179
2.280
3.786
5.032
.000
.000
.015
.000
.000
10.
11.
12.
13.
Responsiveness
Tell customers exactly
Prompt service to customers
Willing to help customers
Respond to customers‟ request
5.269
5.166
5.418
2.397
.029
.000
.000
.000
14.
15.
16.
Confidence in customers
Feel safe in transactions
Courteous with customers
3.598
4.371
6.403
.000
.001
.000
Tangibility
Reliability
Assurance
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
17.
Answer customers‟ questions
18.
19.
20.
21.
22.
Individual attention
Operating hours convenient
Personal attention
Best interest at heart
Understand specific needs
3.362
.000
2.550
2.739
3.637
4.251
4.369
.013
.011
.004
.000
.000
Empathy
Findings from data presented in Table-2 demonstrate significant differences between bank
clients‟ perceptions and expectations of service on all 22 statements. However, for all the
statements, there is a statistical difference at significance level of 0.05, which illustrates a
statistically significant gap between the clients‟ perceptions and expectations of service at the
95% confidence level.
4.3. Confirmatory factor analysis:
Confirmatory factor analysis was selected to refine and validate the measurement scales; it
was identified as an appropriate statistical test particularly as the researchers had a
reasonably sound knowledge of the number of factors that were required to explain the
intercorrelations among the measurement variables (Suresh Chandar et al., 2002). The
current study tested the SERVPERF model in private sector banks in Uttar Pradesh.
4.4. First-order confirmatory factor analysis:
In order to achieve reliability and validity of the measurement model, CFA using Amos 16 was
conducted (Byrne, 2001).
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Table-3
First order confirmatory factor analysis
Standardized
factor loading
(t-score)
Squared multiple
2
correlations R
1.
Modern-looking equipment
3.76 (.636)
2.
Physical facilities
3.39 (.558)
3.
Neat appearing
3.55 (.637)
4.
Visually appealing
3.66 (.711)
Factor-2:Reliability ( scale composite reliability= 0.910 )
1
**
.596(10.46 )
**
.843(12.24 )
**
.744(21.42 )
.431
.652
.711
5.
Promise to do something
3.54 (.757)
6
Sincere interest in solving it
3.83 (.779)
7.
Perform the service
3.66(.794)
8.
Provide the service
3.59 (.895)
9.
Insist on error free records
3.72 (.812)
Factor-3:Responsiveness ( scale composite reliability= 0.904 )
1
**
.734(14.25 )
**
.661(12.71 )
**
.845(11.25 )
**
.749(16.34 )
.573
.485
.517
.473
10.
Tell customers exactly
3.54 (.636)
11.
Prompt service to customers
3.68 (.754)
12.
Willing to help customers
3.57(.753)
13.
Respond to customers‟ request
3.56 (.853)
Factor-4:Assurance ( scale composite reliability= 0.921 )
1
**
.841(08.42 )
**
.703(13.46 )
**
.796(08.66 )
.755
.547
.625
14.
Confidence in customers
3.47 (.840)
15.
Feel safe in transactions
3.96 (.752)
16.
Courteous with customers
3.54 (.589)
17.
Answer customers‟ questions
4.12 (.716)
Factor-5: Empathy ( scale composite reliability= 0.934 )
1
**
.798(17.43 )
**
.872(13.45 )
**
.856(08.85 )
.764
.632
.557
18.
Individual attention
19.
Operating hours convenient
20.
Personal attention
21.
Best interest at heart
22.
Understand specific needs
** Significant at the 0.05 level
1
**
.648(13.54 )
**
.775(16.46 )
**
.845(12.27 )
**
.788(15.98 )
.478
.675
.753
.762
S.No.
Statements
Mean (S.D)
Factor-1:Tangibility ( scale composite reliability= 0.923 )
3.78 (.653)
4.06 (.782)
3.64(.708)
3.63 (.682)
3.57 (.680)
First-order confirmatory factor analysis was conducted for the five-dimensional model of
SERVPERF. To evaluate the fit of CFAs, several goodness-of-fit indicators were used to
assess the model‟s goodness of fit. The measurement model provided an acceptable fit to the
data when considering fit statistics. A completely standardized solution produced by Amos 16
using maximum likelihood method showed that all of the 22 items loaded highly on their
corresponding factors, confirming the unidimensionality of the constructs and providing strong
empirical evidence of their validity. In structural equation modelling, there are some statistical
outputs which can be used to measure the mean, standard deviation, standardized factor
loading, squared multiple correlations R2 for each measurement item, and scale composite
reliability of each factor. As a rule of thumb, measurement variables are reliable when the
squared multiple correlation R2of each one is greater than 0.5 (Holmes-Smith 2001, Byrne
2001). The first run of the measurement model showed that the R 2 for the majority of
measurement items were greater than 0.5, which indicated a good reliability of SERVPERF
model. The final results of the confirmatory factor analysis for the SERVPERF model became
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
stronger. This is reflected by the t-scores ranging from 08.66 to 21.42, indicating that all factor
loadings are significant and providing evidence to support the convergent validity of the items
measured (Anderson and Gerbing, 1988).
.33
e1 .14
e2
.18
e3 .26
1
P1
1.00
1.23
1
P2
1
P3
.13
2.13
2.37
Tangibility
1
P4
e4
.46
e5 .38
e6 .11
e7 .18
e8 .43
1
.13
P5
1.00
.76
1
P6
1
P7
1
P8
.17
1.12
1.57
.36
Reliability
.15
1
P9
e9
.21
P10
e10
.14
e11.22
P11
1
e14.29
e15.27
e16.36
1
P13
e18.47
e20.18
e21.23
e22
.21
Responsiveness
.37
.19
.18
P14
1.00
.54
1
P15
1
P16
.51
.36
.32
Assurance
.11
1
P17
.38
1
P18
1.00
.36
1
P19
e19
.25
.04
1
e17
.19
1.21
.11
P12
e13
.17
1.00
1.32
1
e12
.25
.06
.15
1
1
P20
1
P21
.87
.79
.99
.24
Empathy
1
P22
Figure2: First order confirmatory factor analysis
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
The above figure-2 showed the variance, covariance, unstandardized factor loading and error
variances for the first order confirmatory factor analysis. Table-4 shows the common model-fit
indices, recommended values and results of the test of structural model fitness. The Root
Mean square Error of Approximation (RMSEA) is suggested to be used as a measure of
discrepancy per degree of freedom (Browne & Cudeck, 1993; Steiger, 1990). The lower the
RMSEA values; the better it is, with maximum acceptable values between 0.08 and 0.09.
Further, to eliminate or reduce the dependence of chi-square on sample size, the values of
the Goodness-of-Fit (GFI) and Adjusted Goodness-of-Fit (AGFI), Tucker Lewis index (TLI),
Comparative fit index (CFI) and Normalized fit index (NFI) were used. The score obtained
from the analysis suggested an excellent fit between the data and the model (X 2 =363.43,
degree of freedom = 199, GFI = 0.945, AGFI = 0.937, TLI = 0.947, CFI = 0.952, NFI = 0.963,
RMSEA = 0.062). All the fit indices comply with the values recommended by (Heir et al.,
1998) and Arbuckle and Worthke (1995) including chi-square/ degree of freedom of the
SERVPERF model.
Table- 4: Fit statistics in the first order confirmatory model
S.No.
Goodness- of -fit model index
Recommended
*
value
≤ 2.00
SERVPERF
model
1.826
1
Chi-square/degree of freedom
2
Goodness-of-index (GFI)
≥ 0.90
0.945
3
Adjusted goodness-of-index (AGFI)
≥ 0.90
0.937
4
Tucker –Lewis index (TLI)
≥ 0.90
0.937
5
Comparative fit index (CFI)
≥ 0.90
0.952
6
Normalized fit index (NFI)
≥ 0.90
0.963
7
Root mean square of approximation (RMSEA)
≤ 0.08
0.062
**
*These criteria are according to Arbuckle and Worthke (1995) and Hair et al (1998)
4.5 Construct reliability
The first step is to calculate the Cronbach‟s alpha reliability coefficient in order to assess the
psychometric properties of the SERVPERF instrument.
Table-5: Reliability analysis for SERVPERF dimensions.
Number of items
Cronbach‟s alpha
Tangibility
4
0.923
Reliability
5
0.910
Responsiveness
4
0.904
Assurance
4
0.921
Empathy
5
0.934
Dimensions
Using the Likert scale, it is necessary to calculate the Cronbach‟s alpha coefficient for
reliability and consistency (Joseph et al., 2003) of the instrument. The Cronbach‟s alpha
values for the SERVPERF subscales are 0.923, 0.910, 0.904, 0.921and 0.934 for tangibility,
reliability, responsiveness, assurance, and empathy.
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Proceedings of 29th International Business Research Conference
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4.6 Correlation Analysis
Correlations reflecting the relationship between research variables were positively significant.
Correlation can only reveal the degree of relationship between constructs of SERVPERF
model. To analyze the direct and indirect effect, as well as mediating effect among the
construct, researcher applied structural equation modelling for first order confirmatory factor
analysis.
Table-6
Correlation Analysis
Factors
Mean
SD
Tangibility
Tangibility
3.45
0.654
1
Reliability
3.56
0.781
.452
Reliability
**
.458
**
.434
**
.578
.537
3.33
0.879
Assurance
3.81
0.640
.552
Empathy
3.42
0.773
.531
Assuranc
e
Empath
y
1
**
Responsivene
ss
Responsivene
ss
**
1
**
.333
**
**
.419
**
1
**
.563
1
**Correlation is significant at the 0.05 level (2-tailed).
Conclusion/implications
This study examines the relationship among banking service quality dimensions- tangibility,
reliability, responsiveness, assurance, and empathy through SERVPERF Model. The study
critically examines the service quality issues in the Uttar Pradesh banking system from the
perspective of the customer‟s satisfaction. The results of this research have provided
evidence that the service quality dimensions developed in this research allowed for
differences in the degree to which each individual item contributed to the overall composite
scale, thus providing a more realistic representation of the data. The fit statistic of the first
order confirmatory factor analysis also proved to be a useful instrument to test the validity of
the SERVPERF model. This indicates that the indicator variables contributing to the overall
measurement of the manifest or composite variables all represented the same generic scores,
meaning they are valid measures of the underlying construct of SERVPERF model. The
results also indicate that the gap in all five dimensions – tangibility, reliability, responsiveness,
assurance, and empathy – were statistically significant. Out of the five, three dimensions of
SERVPERF model- reliability, empathy, and responsiveness strongly influenced the clients in
psychological, emotional, and cognitive ways, particularly as the core service becomes
intangible in banking services. Private Banks characterised by such a service orientation are
more likely to offer reliability, empathy, & responsiveness to clients and provide them with the
assurance of improved quality in service delivery which, in turn, leads to higher perceived
service quality from the client‟s point of view. However, only two service quality dimensions –
assurance and tangibility were found to be weak and statistically significant when measuring
client satisfaction. Private Banks ought to train and develop their staff adequately, especially
those dealing directly with clients for different affairs. Banks should train their employees to
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Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
enhance their appealing quality which, in turn, will influence or attract more clients into
choosing their banks. On the other hand, factors such as accessibility of the branch, sufficient
business hours, convenient, sufficient parking lots and encouraging bank responses are
equally important for clients in patronising private banks in Uttar Pradesh.
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