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International Journal of Interdisciplinary Research
1
ISSN 2348-6775 (online)
Vol. 01 Issue 05 Aug 2014
Abstract
“This research paper aims to validate the model of performance of the private sector banks from
the perspectives of rural areas of Punjab’s clients by replicating the factors used in an earlier
study by Cronin and Taylor. The selection criteria examined in this study were the items included
in the SERVPERF measurement and the relative importance of the dimensions of tangibility,
reliability, responsiveness, assurance, and empathy were examined along with other preferences.
Apparently, data was collected through convenience sampling from 200 rural areas’ clients
across the four districts of Punjab. The results confirmed that the model of performance criteria
is multi-dimensional; tangibility, reliability, responsiveness, assurance, and empathy. Researcher
also 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. Out of these five
factors, three factors (reliability, empathy, & responsiveness) resulted in strong significance
while assurance and tangibility were weak significant. This paper attempted to validate a model
based on the perception of rural clients pertaining to the performance of the private sector banks
which will give an insight towards better understanding their attitudes. Further, it will also help
the private sector banking industries in designing marketing strategies according to their clients’
preferences in a different rural background. Finally, the use of SEM in validating the model is
also a valuable contribution to this study”. KEYWORDS: SERVPERF model, rural clients, structural equation modelling.
INTRODUCTION
In banking sector, service quality is related to bank loyalty through satisfaction (Bloemer et al.,
1998). Indian banking industry is witnessing keen competition in acquiring new and retaining
existing customers. Private sector banks in India have to compete with, public sector banks and
multinational banks. The objective of this study was to explore the impact of individual aspects
of banking operation on various types of clients‟ perceptions of service quality. In this study, data
were collected based upon the bank clients‟ perception about the five- factor pertaining
SERVPERF. Data were collected from 200 customers of private sector banks such as ICICI,
IDBI, HDFC, AXIS, and Bank of Punjab from rural areas of like Ludhiana, Amritsar, Jalandhar
and Chandigarh, Punjab. “ICICI Bank has opened over 130 rural branches in Punjab & Haryana.
The addition of these new branches has increased the network of ICICI Bank in Punjab &
Haryana to more than 260 branches”. Several studies done on the selection criteria on services
focused on the retail banking services (Haron, et al., 1994; Zineldin, 1996; Levesque &
McDougall, 1996; Almossawi, 2001; Babakus et.al., 2004). This study intends to replicate the
SERVPERF measures designed by Cronin and Taylor (1992), and apply it on private sector
banks in rural area of Punjab. The basis of the scale measurement used for this study follows that
of Cronin‟s and Taylor‟s (1992) study that used SERVQUAL items. The five dimensions of
SERVQUAL scale (Parasuraman et.al., 1988) include the tangibility, reliability, responsiveness,
assurance, and empathy. However, it has been empirically demonstrated that the measures of the
Structural Equation Modeling with AMOS to confirm the dimensions of
SERVPERF model in private sector banks
Dr. Priyaka Khanna, Assistant Professor, Department of Commerce
Khalsa College for Women, Ludhiana-Punjab
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service performance (SERVPERF) constitute more effective measure than SERVQUAL (Cronin
& Taylor, 1992, 1994). SERVPERF explained more of the variation in the global measure of
service quality in all of the four service industries Cronin and Taylor (1994) examined: banks,
pest control, dry cleaning, and fast food services. Researcher also intends to adopt the
SERVPERF to measure the banking service quality in Punjab. Moreover, service quality is
consistently viewed in the literature as a unique construct of client satisfaction. It is the client‟s
satisfaction that influences the success and performance of a private sector banks. Therefore, it is
very important to assess the client‟ perception of service quality and the degree of satisfaction
with different services and products provided by private sector banks. In this respect, important
questions may be raised. For example, what are the factors that motivate clients to bank with
rural areas of Punjab and to what extent are clients satisfied with the financial services offered by
private sector banks in Punjab where public sector banks (Bank of Baroda, Punjab National
Bank, Bank of India, Canara Bank, Central Bank of India, Indian Bank, Indian Overseas Bank,
Syndicate Bank, UCO Bank, Allahabad Bank, United Bank of India, Oriental Bank of
Commerce, Corporation Bank, Vijaya Bank, Dena Bank, Bank of Maharashtra, Andhra Bank,
Punjab & Sind Bank, New Bank of India, and Corporation Bank) also operate? Therefore, this
study aims to investigate the degree of client satisfaction with current services provided by
private sector banks (ICICI, IDBI, HDFC, AXIS, and Bank of Punjab) in Punjab. The issue of
service quality is of great importance in Punjab for many reasons. The first is the lack of research
on how to manage service quality in the private sector banking industry. Secondly, private sector
banks are growing rapidly in the rural areas of Punjab.
LITRATURE REVIEW Service quality
Most of the studies in the area of service quality has been based upon the model developed by
Parasuraman et al. (1985, 1988), as mentioning five dimensions such as tangibility,
responsiveness, reliability, assurance, and empathy. In 1992, Cronin and Taylor proposed the
alternative method, referred to as SERVPERF. They argued that, to assess service quality,
perception of customers regarding the performance of service provides better results than using
SERVQUAL. Some researchers in 1994, Parasuraman et al. also mentioned that measurement
method using SERVPERF is better than using SERVQUAL, though SERVQUAL can provide
better diagnostic results of service quality. When SERVQUAL applied for banking sector,
problems were identified with regard to its dimensionality and the value of expectation scores
(Lam, 1995). Many researchers attempted to explain and measure the concept of service quality
(Carman, 1990; Cronin & Taylor, 1992; Parasuraman et al., 1985, 1988, 1991). According to
Natarajan et al. (1999) attempted to evaluate the service quality in the banking industry with a
customer survey using the items used in the SERVQUAL instrument and Lassar et al. (2000)
used two techniques to assess the service quality in private banking. The first technique adopted
the SERVQUAL instrument and the second technique was a measure of functional/technical
quality. Their results provided initial support for SERVQUAL without excluding the use of both
techniques in different settings and contexts. In a recent study, Naceur and Al-Tamimi (2003)
used an instrument including 30 items belonging to the five dimensions of SERVQUAL in
United Arab Emirates (UAE) banks. One another study in Australian banking industry, Avkiran
(1994) attempted to explain BANKSERV instrument based on SERVQUAL model. Avkiran
(1994) evaded the probable psychometric difficulties connected with SERVQUAL (Avkiran,
1999:62). BANKPERFs major advantage over BANKSERV is that there is strong theoretical
support for a performance only measure of service quality (Cronin and Taylor, 1992, 1994). The
current study employed SERVPERF model (22 items) to measure the service quality of private
sector banks in rural Punjab.
Customer satisfaction
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Customer Satisfaction is customers‟ collective conception of a firm‟s service performance
(Johnson and Fornell, 1991). Banking selection criteria are expected to affect a customer‟s
overall satisfaction towards his or her bank (Levesque and McDougall, 1996). Indeed, customer
satisfaction is considered to be an important factor in the success of private sector banks and its
continuity in the market place. The present study finds the relationship between overall client
satisfactions with service quality dimensions in rural areas of Punjab state.
Thus, this study attempts to fill the gap by providing insights into clients‟ perceptions toward
service quality dimensions in rural Punjab. Thus, in this study, appropriate service quality
dimensions employed. On this basis, this study will be, potentially, relevant to private sector
banks that wish to know which service quality dimension will influence client satisfaction, as
well as the bank selection decision of clients in rural Punjab. The above discussions lead to the
study issue: how do service quality dimensions affect client satisfaction in private sector banks.
PURPOSE AND RATIONALE The main objective of the study: To develop a model based on the selection criteria replicated
from Cronin and Taylor (1992) and examines the relationship between service quality
dimensions and overall client satisfaction.
METHODOLOGY Research instrument
The survey questionnaire for the present research was designed based on the SERVPERF items,
adopted from Cronin and Taylor (1992). Cronin and Taylor (1992) used the performance-only
measures of SERVQUAL originally designed by Parasuraman et al. (1988). A five-point scale (1
indicating strongly disagree and 5 indicating strongly agree) was used in preference to a five-
point scale to increase the sensitivity of the measure. In this study, private banks client‟s
perceptions were measured with a self administered questionnaire. Researcher divided
questionnaire into three parts. The first part began with general statements about the respondent‟s
banking information. Part two dealt with the SERVPERF five constructs and consists 22 items.
Finally, part three consisted of the respondent‟s demographic variables such as genders, age
groups, education backgrounds, and occupations and monthly income.
Data Collection
The survey questionnaire is design and distributed to 200 private banks‟ clients. All 200 clients
had an account five private banks such as ICICI, IDBI, HDFC, AXIS, and Bank of Punjab.
Banks‟ clients are the rural people who are at the legal age to hold a savings account in any of the
private sector banks in rural Punjab (Ludhiana, Amritsar, Jalandhar, and Chandigarh). Sampling
method that use in this study is non-probability sampling i.e. convenience. Thus, the survey
questionnaires are designed to apply to a heterogeneous population, where targeted respondents
come from the general public (from difference genders, age groups, education backgrounds, and
occupations). From the total of 200 respondents, 24.4% are between 21-30 yrs, 28.6% are
between 31-40 yrs, 34.4% are between 41-50 yrs, 8.2 are between 51-60 yrs, and only 4.4%
respondents are 61yrs and above. In terms of age category 68.3% are male and 31.7% are
male within which 7.4% are intermediate, 56.2% are graduates, 28.5% are post graduates,
and remaining 7.9% are professionals. Among the respondents their occupations
categories, 5.6% are government job, 24.5% are private job holders, 51.6 % are self-
employed and rest of the 13.1% are students.
Data analysis
Collected data were analyzed with the help of software package SPSS (20) and analysis of
moment structure (AMOS17). Statistical techniques like descriptive analysis, reliability analysis,
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validity analysis, and confirmatory factor analysis were used to evaluate the service quality.
Structural equation modelling (SEM) was used to validate the conceptual model.
RESULTS AND DISCUSSION The current study employed various statistical techniques to calculate the demographic profile of
respondents and other tests. In addition, normality test conducted to check the normality
distribution of the data‟s skewness and kurtosis. The researcher then performed the confirmatory
factor analysis to test the measurement model specifying the posited relations of the observed
variables to the underlying construct. Both first-order confirmatory factor analysis models and
the second-order confirmatory factor analysis model for SERVPERF were run. The researcher
performed structural equation modeling to determine the relationship between clients‟
satisfaction and the underlying construct of SERVPERF model. Researcher tested reliability
analysis to check for the internal consistency of the sub scale (tangibility, reliability,
responsiveness, assurance, and empathy) and overall SREVPERF instrument by applying
Cronbach‟s alpha test. Finally, researcher tested the content, convergent and discriminant validity
of the SERVPERF instrument.
Normality Test Researcher employed normality test for the sample of rural Punjab indicated that the absolute
values of skewness and kurtosis were -0.809 and 2.778 respectively. These values were less than
the respective values of 3 for skewness and 8 for kurtosis as suggested by Kline (2005).
Table-1: Normality Test
Variables Minimum Maximum Skewness C.R Kurtosis C.R
Tangibility
Modern-looking equipment 1.000 5.000 -.809 -3.026 2.778 5.196
Physical facilities 2.000 5.000 .386 1.442 -.546 -1.022
Neat appearing 2.000 5.000 .278 1.042 .004 .008
Visually appealing 2.000 5.000 .285 1.065 -.658 -1.231
Reliability
Promise to do something 1.000 5.000 -.574 -2.149 .860 1.609
Sincere interest in solving it 2.000 5.000 -.206 -.769 -.334 -.625
Perform the service 1.000 5.000 -.492 -1.842 .366 .685
Provide the service 1.000 5.000 -.308 -1.151 .130 .244
Insist on error free records 2.000 5.000 .283 1.060 -.716 -1.339
Responsiveness
Tell customers exactly 2.000 5.000 .093 .350 -.361 -.676
Prompt service to customers 2.000 5.000 .246 .920 -.641 -1.199
Willing to help customers 2.000 5.000 .207 .774 -.478 -.894
Respond to customers‟ request 1.000 5.000 -.303 -1.135 -.002 -.003
Assurance
Confidence in customers 1.000 5.000 .054 .201 .041 .077
Feel safe in transactions 2.000 5.000 -.023 -.087 -.677 -1.266
Courteous with customers 2.000 5.000 .620 2.320 -.286 -.535
Answer customers‟ questions 3.000 5.000 -.034 -.126 -.998 -1.867
Empathy
Individual attention 3.000 5.000 .091 .339 -.497 -.929
Operating hours convenient 2.000 5.000 -.406 -1.521 -.363 -.679
Personal attention 1.000 5.000 -.185 -.692 .491 .918
Best interest at heart 2.000 5.000 .063 .236 -.503 -.941
Understand specific needs 3.000 5.000 .312 1.167 -.793 -1.484
C.R- critical ratio
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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 rural Punjab.
First-order confirmatory factor analysis
In order to achieve reliability and validity of the measurement model, CFA using Amos 16 was
conducted (Byrne, 2001).
Table-2: first order confirmatory factor analysis
S.No. Statements Mean (S.D)
Standardized
factor loading
(t-score)
Squared multiple
correlations R2
Factor-1:Tangibility ( scale composite reliability= 0.923 )
1. Modern-looking equipment 3.25 (.548) 1 -
2. Physical facilities 3.46 (.227) .694(11.43**
) .481
3. Neat appearing 3.45 (.575) .873(17.26**
) .762
4. Visually appealing 3.64 (.678)
.784(22.45**
) .751
Factor-2:Reliability ( scale composite reliability= 0.910 )
5. Promise to do something 3.65 (.457) 1 -
6 Sincere interest in solving it 3.54 (.584) .757(15.28**
) .573
7. Perform the service 3.24 (.564) .697(13.81**
) .485
8. Provide the service 3.52 (.842) .843(10.25**
) .710
9. Insist on error free records 3.88 (.747)
.785(16.35**
) .440
Factor-3:Responsiveness ( scale composite reliability= 0.904 )
10. Tell customers exactly 3.33 (.665) 1 -
11. Prompt service to customers 3.58 (.487) .854(08.22**
) .729
12. Willing to help customers 3.29 (.586) .703(12.47**
) .494
13. Respond to customers‟ request 3.15 (.735)
.796(09.64**
) .427
Factor-4:Assurance ( scale composite reliability= 0.904 )
14. Confidence in customers 3.76 (.475) 1 -
15. Feel safe in transactions 3.26 (.689) .784(18.39**
) .614
16. Courteous with customers 3.47 (.576) .872(12.21**
) .760
17. Answer customers‟ questions 4.76 (.628) .756(08.26**
) .518
Factor-5: Empathy ( scale composite reliability= 0.904 )
18. Individual attention 3.44 (.571) 1 -
19. Operating hours convenient 3.52 (.754) .636(12.14**
) .404
20. Personal attention 3.24 (.777) .775(17.46**
) .600
21. Best interest at heart 3.66 (.853) .845(13.87**
) .714
22. Understand specific needs 3.28 (.678) .788(15.39**
) .802
** Significant at the 0.05 level
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First-order and second-order confirmatory factor analysis were 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. Researcher performed confirmatory
factor analysis on the five dimensions: tangibility, reliability, responsiveness, assurance, and
empathy. Researcher tried to asses overall fit of the model. 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 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 R2 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 stronger. This is reflected by the t-scores ranging from 08.22 to
22.45, indicating that all factor loadings are significant and providing evidence to support the
convergent validity of the items measured (Anderson and Gerbing, 1988).
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.24
Empathy
.41
Assurance
.17
Responsiveness
.27
Reliability
.03
Tangibility
P4
.35
e4
2.26
1 P3
.16
e3
3.04 1 P2
.26
e2 1.45 1
P1
.39
e1 1.00
1
P9
.53
e9
.43
1 P8
.15
e8
1.60 1
P7
.09
e7 1.49 1
P6
.35
e6 .98 1
P5
.30
e5 1.00
1
P13
.38
e13
.36 1
P12
.23
e12
1.34 1 P11
.17
e11 1.48 1 P10
.23
e10 1.00
1
P17
.41
e17
.47
1 P16
.23
e16
.68 1 P15
.33
e15 .68 1 P14
.26
e14 1.00
1
P22
.21
e22
.99
1 P21
.26
e21
.93 1
P20
.29
e20 1.12 1
P19
.58
e19 .46 1
P18
.15
e18 1.00
1
.06
.13
.20
.27
.05
.05 .20
.17
.13
.08
Figure 1: First order confirmatory factor analysis
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The above figure-1 showed the variance, covariance, unstandardized factor loading and error
variances for the first order confirmatory factor analysis. Table-3 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 (X2
=360.100, degree of freedom = 199, GFI =
0.910, AGFI = 0.902, TLI = 0.925, CFI = 0.907, NFI = 0.961, RMSEA = 0.059) 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- 3: Fit statistics in the first order confirmatory model
S.No. Goodness- of -fit model index Recommended
value*
SERVPERF model
1 Chi-square/degree of freedom**
≤ 2.00 1.810
2 Goodness-of-index (GFI) ≥ 0.90 0.910
3 Adjusted goodness-of-index (AGFI) ≥ 0.90 0.902
4 Tucker –Lewis index (TLI) ≥ 0.90 0.925
5 Comparative fit index (CFI) ≥ 0.90 0.907
6 Normalized fit index (NFI) ≥ 0.90 0.961
7 Root mean square of approximation (RMSEA) ≤ 0.08 0.059
*These criteria are according to Arbuckle and Worthke (1995) and Hair et al (1998)
Second-order confirmatory factor analysis
The second-order confirmatory factor analysis model for perceived service quality was designed
to test the relationships between five sub- dimensions (tangibility, reliability, responsiveness,
assurance and empathy) and SERVPERF dimension of perceived service of banks as illustrated
in Figure-2.
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Empathy
Assurance
Responsiveness
Reliability
Tangibility
0.67
P4 e4
0.73
0.35
P3 e3 0.64
0.45
P2 e2 0.84
0.32
P1 e1 0.75
0.94
P9 e9
0.88 0.31
P8 e8
0.91
0.37
P7 e7 0.7
0.64
P6 e6 0.83
0.54
P5 e5 0.72
0.5
P13 e13
0.86
0.78
P12 e12 0.73
0.45
P11 e11 0.81
0.57
P10 e10 0.78
0.58
P17 e17
0.95
0.83
P16 e16 0.64
0.54
P15 e15 0.67
0.28
P14 e14 0.75
0.29
P22 e22
0.7 0.81
P21 e21
0.78
0.47
P20 e20 0.65
0.49
P19 e19 0.68
0.76
P18 e18 0.77
SERVPERF
0.47
0.79
0.65
0.48
0.61
Res 1
0.34
Res 2
0.62
Res 3
0.5
Res 4
0.81
Res 5
0.77
Figure 2: Second order confirmatory factor analysis
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Table-4: second order confirmatory factor analysis
S.No. Statements Standardized factor
loadings (t- value)
Squared multiple
correlations R2
Tangibility
.47(03.56**
)
1 Modern-looking equipment .75(14.63**
) .56
2 Physical facilities .84(06.45**
) .70
3 Neat appearing .64(08.53**
) .40
4 Visually appealing .73**
Reliability .79(09.47**
)
5 Promise to do something .72(12.78**
) .51
6 Sincere interest in solving it .83(16.47**
) .68
7 Perform the service .70(11.72**
) .49
8 Provide the service .88(09.47**
) .77
9 Insist on error free records .60**
Responsiveness .65(06.95**
)
10 Tell customers exactly .78(15.27**
) .60
11 Prompt service to customers .81(11.23**
) .65
12 Willing to help customers .73(08.14**
) .53
13 Respond to customers‟ request .86**
Assurance .48(04.33**
)
14 Confidence in customers .75(08.46**
) .56
15 Feel safe in transactions .67(11.57**
) .44
16 Courteous with customers .64(10.34**
) .40
17 Answer customers‟ questions .95**
Empathy .61(07.06**
)
18 Individual attention .77(12.49**
) .59
19 Operating hours convenient .68(07.48**
) .46
20 Personal attention .65(11.50**
) .42
21 Best interest at heart .78(15.93**
) .60
22 Understand specific needs .65**
** Significant at the 0.05 level
Measurement variables are reliable when the squared multiple correlation R2of each one is
greater than 0.5.The second order confirmatory factor analysis showed that the R2 for the
majority of measurement items were greater than .5, which indicated a good reliability of
SERVPERF model. Results of the second order factor analysis indicates that t-scores ranging
from 06.45 to 16.47, indicating that all factor loadings are significant and providing evidence to
support the convergent validity of the items measured. All standardized factor loadings were
statistically significant (t-values > 1.96), and ranged from 0.64 to 0.95, which were above the
recommended threshold of 0.60 as suggested by Bagozzi and Yi (1988), confirming adequate
convergent validity. These results supported the reliability and validity of the measures
associated with the second-order confirmatory factor analysis model for perceived service quality
of private sector banks in rural Punjab. The results shows that the factor loading values
associated with the five first-order factors indicated that reliability (λ = 0.79, t-value = 09.47, p <
0.00) was the strongest indicator of the second-order confirmatory factor analysis followed by
empathy (λ =0.61, t-value= 07.06, p < 0.00), responsiveness (λ = 0.65, t-value = 06.95, p < 0.00)
, assurance (λ =0 .48, t-value = 04.33 ,p < 0.00), and tangibility (λ = 0.47, t-value = 03.56, p
<0.00).
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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. According to Sekaran (2003), he
recommended to ensure the stability of the consistency of the research instrument. Even though,
we adopted a well established SERVPERF instrument given by Cronin & Taylor, (1992), it is
very necessary to see the consistency of the instrument in the settings of the rural Punjab study.
Table-5: Reliability analysis for SERVPERF dimensions.
Dimensions Number of items Cronbach‟s alpha
Tangibility 4 .874
Reliability 5 .723
Responsiveness 4 .816
Assurance 4 .891
Empathy 5 .857
Overall SERVPERF 22 .910
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 .874, .723, .816, .891 and .857 for tangibility, reliability,
responsiveness, assurance, and empathy. The Cronbach‟s alpha value for the SERVPERF
instrument is .910, which indicate greater stability and consistency of the research instrument,
however for basic research the cut-off value is 0.60 (Nunnally, 1978).
Reliability Analysis
Reliability analysis is important to standardise the measurement scales, and to demonstrate
whether they truly measure what they are supposed to measure, Cronbach‟s alpha coefficient and
item to total correlations are both used to measure the internal consistency of each identified
construct. The reliability of the construct is acceptable if Cronbach‟s alpha exceeds 0.70 and
item-to-total correlations have greater than 0.50 (Hair, Anderson, Tatham & Black, 1998). Mean,
standard deviation, item-to-total correlations, and Cronbach‟s alpha coefficient, are listed in
Table-6 which shows that this research has achieved the high reliability (Cronbach‟s α coefficient
all above 0. 70 and item-to total correlations most around 0.50). However, these scales had items-
total correlation of over 0.30, which is a general criterion for acceptable reliability.
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Table-6: Reliability Analysis
Statements Mean (SD)
Corrected Item-
Total
Correlation
Cronbach's
Alpha if Item
Deleted
Tangibility
Modern-looking equipment 3.25 (.548) 0.565 0.765
Physical facilities 3.46 (.227) 0.752 0.742
Neat appearing 3.45 (.575) 0.679 0.762
Visually appealing 3.64 (.678) 0.575 0.885
Reliability
Promise to do something 3.65 (.457) 0.549 0.714
Sincere interest in solving it 3.54 (.584) 0.687 0.731
Perform the service 3.24 (.564) 0.756 0.847
Provide the service 3.52 (.842) 0.551 0.874
Insist on error free records 3.88 (.747) 0.653 0.795
Responsiveness
Tell customers exactly 3.33 (.665) 0.542 0.741
Prompt service to customers 3.58 (.487) 0.653 0.745
Willing to help customers 3.29 (.586) 0.673 0.814
Respond to customers‟ request 3.15 (.735) 0.560 0.864
Assurance
Confidence in customers 3.76 (.475) 0.664 0.778
Feel safe in transactions 3.26 (.689) 0.586 0.742
Courteous with customers 3.47 (.576) 0.652 0.717
Answer customers‟ questions 4.76 (.628) 0.679 0.846
Empathy
Individual attention 3.44 (.571) 0.696 0.711
Operating hours convenient 3.52 (.754) 0.576 0.832
Personal attention 3.24 (.777) 0.552 0.755
Best interest at heart 3.66 (.853) 0.646 0.825
Understand specific needs 3.28 (.678) 0.632 0.795
The corrected items-total correlation and Cronbach‟s α coefficient for bank clients‟ perception
shown in Table-4. All of the scales had Cronbach‟s α coefficient above 0.70, each of the scales
whose Cronbach‟s α coefficient was above 0.60 had items-total correlations greater than 0.30,
and thus were retained for analysis.
Validity Analysis
The present study, various validity terms were used to demonstrate different aspects of construct
validity. This research utilised content, convergent, and discriminant validity to indicate the
ability of the measurement items to measure accurately the constructs of the study (Hair et al.
1995). Validity indicates the degree to which an instrument measures the construct under
investigation.
Content Validity
The importance of content validity is subjective agreement among professionals that a scale
logically appears to reflect accurately what it purports to measure (Zikmund, 2000). Most
research in the area of SERVQUAL has been based upon the model developed by Parasuraman
et al. (1985, 1988), which incorporates a comparison of customer expectations and perceptions of
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service performance in banking sector. Content validity means whether the content of
questionnaire reflects the subjects of the study or not. It also checks whether the measurement
selected by the researchers can measure the topic correctly. Because of being constructed based
upon previous research relating to SERVPERF instrument, the questionnaire in the banking study
shows content validity.
Convergent Validity
In this study, convergent validity is recognised when the relationship between SERVPERF items
and the five dimensions of instrument significantly different from zero. Based on this criterion,
critical ratios can be used to evaluate the statistical significance for first and second order
confirmatory factor analysis. Parameters which have a critical ratio greater than 1.96 can be
considered significant based on the level of p < 0.05 (Anderson & Gerbing 1988). In relation to
the present study, the entire SERVPERF model (22 items) represented their dimensions
significantly, as the critical ratio of every item exceeded the 1.96 value; hence, all of the
SERVPERF items satisfied the convergent validity test for private sector banks in rural Punjab.
Discriminant validity
Discriminant validity, on the other hand, measures the extent to which the latent variables are
different (Zikmund 2003). It refers to whether observed constructs that are highly related to each
other (Campbell & Fiske, 1959; Gaski, 1984)). Table-6 shows the result of the calculated
variance extracted (VE) to support discriminant validity of constructs. This approach suggested
by Fornell & Larker (1981) is that discriminant validity is demonstrated when the squared
correlation between two constructs is lower than the respective average variance extracted. This
table shows the comparison between squared correlations of two construct (diagonal elements).
Overall of the five constructs tangibility, reliability, responsiveness, assurance, and empathy
show evidence of high discriminant validity. Therefore, the measurement model exhibited a good
level of model fit as well as evidence of convergent validity and discriminant validity. The
measures indicators were then deemed adequate for further analysis of structural model.
Table-7: Discriminant validity
Factors Tangibility Reliability Responsiveness Assurance Empathy
Tangibility
0.654**
Reliability 0.322 0.573**
Responsiveness 0.315 0.274 0.631**
Assurance 0..258 0.340 0.223 0.614**
Empathy 0.307 0.327
0.342 0.216 0.608**
Note: Diagonal elements are average variance extracted (AVE) for each of the five constructs.
Off-diagonal elements are the squared correlations between constructs. (p<.05)
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Correlation analysis: Means, standard deviations, and inter-correlations for the banking
study variables are reported in Table-8. Correlations reflecting the relationship between research
variables predicted by the hypotheses were positive 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, we applied structural equation
modelling for first and second order confirmatory factor analysis.
Table-8: Correlation analysis
Factors Mean SD Tangibility Reliability Responsiveness Assurance Empathy
Tangibility 3.45 0.654
1
Reliability 3.56 0.781
.452**
1
Responsiveness 3.33 0.879
.537**
.458**
1
Assurance 3.81 0.640
.552**
.434**
.333**
1
Empathy 3.42 0.773
.531**
.578**
.419**
.563**
1
**Correlation is significant at the 0.05 level (2-tailed).
CONCLUSION AND IMPLICATIONS
The aim of this research was to carry out an empirical analysis of the constructs determining the
private banks clients‟ perception about the five dimensions- tangibility, reliability,
responsiveness, assurance, and empathy pertaining to SERVPERF model, using a structural
equation modelling with the help of AMOS software. This study affirms an instrument of service
quality in the context of private sector banking in rural areas of Punjab, and examines the
relationship among banking service quality dimensions- tangibility, reliability, responsiveness,
assurance, and empathy. The proposed model SERVPERF was employed and then calibrated
using the data collected from saving accounts clients of private sector banks. The study critically
examines the service quality issues in the Punjab banking system from the perspective of the
rural clients. 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 and second order confirmatory factor
analysis also proved 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 all the five
dimensions – tangibility, reliability, responsiveness, assurance, and empathy – were statistically
significant. Out of the three dimensions of SERVPERF model- reliability, empathy, &
responsiveness strongly influenced the clients in psychological, emotional, and cognitive ways,
particularly as the core service becomes intangible in banking services. Private Banks
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characterised by such a service orientation is more likely to offer a reliability, empathy, &
responsiveness to clients and provide them with the assurance in conveying trust and confidence
that will result in improved quality in service delivery which, in turn, leads to higher perceived
service quality from the client point of view. However, only two service quality dimensions –
assurance and tangibility were found to be weak statistically significant when measuring client
satisfaction. From the managerial perspective, the instrument developed and used in this research
will be very useful to bankers and policy makers as a tool to determine the service quality factor
of the private sector banks in rural Punjab. Private Banks ought adequately to train and develop
their staff, especially with respect to the rural clients. Banks should train their employees to
enhance the quality of rural banking staff 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 rural places in Punjab.
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