International Journal of Interdisciplinary Research - · PDF fileInternational Journal of...

International Journal of Interdisciplinary Research

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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

mailto:[email protected]


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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


13



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)


14



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


15



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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