Sem in Servqual

Problem Definition

Defining service quality and its components in a form that is actionable in the workplace

is an important endeavor that an organization should not take lightly. Without a clear and

unambiguous definition, employees will be left with vague instructions on improving

service quality within the workplace. The result will be that each employee will be left to

form and act upon his or her own definition of quality which, more often than not, may

be incomplete or inaccurate. Fortunately, there are researchers such as Grönroos (1983),

Lehtinen and Lehtinen (1982), and Parasuraman, Zeithaml and Berry (hereafter referred

to as PZB) (1985) who are working to uncover the factors that determine service quality

and to provide a number of actionable tools that a marketer can use to gauge his or her

firm’s performance. Companies should increase their performance and implication of the

marketing practices that should be based on the variables that best contribute to each of

the dimensions or constructs’ variables.

Problem Statements

Developing and analyzing a confirmatory factor analysis model in case of the service

quality issue and making related measurement and structural model with proper

evaluation of reliability and validity.

Objective of the study

The main purpose of the study is to develop and analyze a confirmatory factor analysis of

Structural Equation Modeling to find the significance of the various constructs of

Servqual for the proper decision making of each marketing program.

Theoretical Framework (literature review)

This paper will review and analyze the literature on service quality, particularly those that

delineate its components as well as those that provide links to behavioral intentions. It

will also critically analyze SERVQUAL, a survey tool put forth by PZB based on their

1

findings, and show that it is an inadequate tool for measuring service quality. The paper

is organized to present the dimensions of service quality and possible future directions of

the service quality literature.

Many scholars agree that service quality can be decomposed into two major dimensions

(Grönroos, 1983; Lehtinen and Lehtinen, 1982). The first dimension is concerned with

what the service delivers and is referred to by PZB (1985) as “outcome quality” and by

Grönroos (1984) as “technical quality”. The second dimension is concerned with how the

service is delivered: the process that the customer went through to get to the outcome of

the service. PZB (1985) refer to this as “process quality” while Grönroos (1984) calls it

“functional quality”. However, while PZB (1985) and PZ (2006) confirmed these

distinctions, they often confusingly use “service quality” when they mean “service

process quality.” Thus to avoid any 2 of 16further confusion a distinction will be made

between “service process” and “service outcome”. Whenever the word service is used, it

should be taken as the total service which is a combination of process and outcome.

Likewise, service quality shall be used to refer to the totality of process quality and

outcome quality.

PZ define service quality as “the degree and direction of discrepancy between customers’

service perceptions and expectations” (2006). Thus if the perception is higher than

expectation, then the service is said to be of high quality. Likewise, when expectation is

higher than perception, the service is said to be of low quality. Realising that there was

not enough literature to produce a rigorous understanding of service quality and its

determinants, PZB (1985) conducted an exploratory investigation to formally delineate

service quality. One of the results of this investigation was the identification of ten

determinants of service process quality. PZB (1985) listed them as follows:

• RELIABILITY involves consistency of performance and dependability.

• RESPONSIVENESS concerns the willingness or readiness of employees to provide

service.

• COMPETENCE means possession of the required skills and knowledge to perform the

service.

2

• ACCESS involves approachability and ease of contact.

• COURTESY involves politeness, respect, consideration, and friendliness of contact

personnel (including receptionists, telephone operators, etc.).

• COMMUNICATION means keeping customers informed in language they can

understand and listening to them. It may mean that the company has to adjust its language

for different consumers—increasing the level of sophistication with a well-educated

customer and speaking simply and plainly with a novice.

• CREDIBILITY involves trustworthiness, believability, honesty. It involves having the

customer’s best interests at heart.

• SECURITY is the freedom from danger, risk, or doubt.

• UNDERSTANDING/KNOWING THE CUSTOMER involves making the effort to

understand the customer’s needs.

• TANGIBLES include the physical evidence of the service.

In a later paper, PZB (1988) found certain overlaps among the dimensions and shortened

the list into five dimensions. This new list retained tangibles, reliability, and

responsiveness while competence, courtesy, credibility, and security were combined into

a new dimension called assurance. Access, communication, and understanding the

customer, on the other hand, were placed under a common dimension called empathy.

Thus the dimensions are now known as follows:

• Assurance - Knowledge and courtesy of employees and their ability to inspire trust and

confidence

• Empathy - Caring, individualized attention the firm provides its customers.

• Reliability - Ability to perform the promised service dependably and accurately.

• Responsiveness - Willingness to help customers and provide prompt service.

•Tangibles - Appearance of physical facilities, equipment, personnel, and communication

materials.

In their 1988 revision, PZB claim that these five dimensions are generic and consistent

across different types of services by stating that there was “consistent factor structure…

across five independent samples.” However, basing this conclusion on a small sample

3

raises doubts on its validity. Buttle (1996) found serious concerns with the number of

dimensions as well as their consistency in different contexts. Carman (1990), after

conducting a research which involved testing the five dimensions in services other than

those that were used by PZB, warns that “while the PZB items provide a start for item

development, all items need to have validity and reliability checks before commercial

application.” Carman (1990) further states that the 4 of 16dimensions may have been

over-generalized and suggests that some items of the ten dimensions that were no longer

explicitly stated in the five dimensions be retained until further factor analysis shows that

they really are not unique. Peter et al. (1993) also suggest that the overlap between

responsiveness, assurance, and empathy was understated by PZB in their original study.

Woo and Ennew (2005), meanwhile, found that in business services markets, the

dimensions were completely different. Thus, at its best, the five dimensions should only

be considered as a starting point rather than a tool that can be immediately used in the

field. In their papers, PZB (1985, 1988) and PZ (2006) consistently refer to the list as

determinants or dimensions of service quality. However, it appears, from their definition

of each dimension that they are only referring to process quality rather than total service

quality. Woo and Ennew (2005) confirm this finding when they stated that PZB’s work

on service quality dimensions and the subsequent SERVQUAL tool (discussed in a later

section) seemed to neglect technical quality altogether and focus mostly on the functional

side. Furthermore, Richard and Allaway (1993) clearly state that the dimensions of

service quality as it is described by PZB totally neglects technical quality. Parasuraman,

in a later work specified that “service” and “services” mean different things (1998).

Services (plural), according to him, refer to the intangible core product that a business

provides to the firm. In contrast, service (singular) refers to the supplement that

accompanies the core offering. Essentially, he uses services to refer to outcome quality,

while service to refer to process quality. Because of this poor choice of words,

Parasuraman only added further confusion.

Assuming that a better set of words has been selected by PZB, the fact that their model is

focused only on process quality still remains. Asubonteng, McCleary, and Swan (1996),

on the other hand, defend PZB’s model by stating that because outcome quality is

4

difficult to evaluate for any service, customers will often rely on other characteristics of

the service to determine its quality. That is, they will rely on the process quality to

determine or make an approximation of the total service quality. Unfortunately,

Asubonteng, McCleary, and Swan did not provide any empirical data to confirm this.

Their claim that outcome quality is difficult to evaluate for “any service” is flawed and

some examples that disprove their statement easily come to mind. Consider the case of a

machine shop that is involved in providing machine 5 of 16 repair services to business

and individual customers. After the service has been provided, the customer is able to

measure outcome quality by comparing the outcome against the specifications it provided

to the machine shop before the start of the service. In another case, this time a plumbing

service where a homeowner has requested the plumber to repair a leaking faucet, the

homeowner is able to measure the quality of the outcome by checking if the faucet is still

dripping. Apart from this, Richard and Allaway (1993) found that PZB’s model—

measuring only process quality—was less reliable than another model that measured both

process and outcome quality. Thus, PZB’s five dimensions of service quality, while

useful as a starting point, is an inadequate tool for measuring a firm’s total service

quality.

5

Conceptual Framework

Hypothesis

H0: The developed models are not fit with reliability and validity

H1: The developed models are fit with reliability and validity

6

Conclusion, Recommendations

and Opportunities for further study

Confirmaratoy Factor Analysis measuring

unobserved concepts by testing measurement

reliability and validity

Primary Data AnalysisInformation on consumers and banking services along with the Judgment for observed and unobserved issues

SurveySecondary data analysis Literature review

Problem Identification

Variables in the Problem:

The latent constructs and their observed variables are

Constructs Variables

Tangibility Up to date Equipment

Physical facility

Dress and grooming of the service

provider

Reliability Promise of the service

Interest of the service provider

Dependability

Right at the first time

Exact time of performance

Responsiveness Prompt service

Ready to respond

Knowledgeable employee

Assurance Safety

Trust with the service

Billing system accuracy

Empathy Polite behavior

Individualized attention

Understanding specific needs

Methodology

The data of this project has been collected both from primary and secondary sources of

information. Primary data have been collected from the respondents of through

questionnaire. And the secondary data collected from various published materials and

internet resources.

7

Type of Research

It is a quantitative research including survey data as well as secondary data for

confirmatory factor analysis under Structural Equation Modeling.

Data Collection Method

Data collected through survey questionnaire.

Sources of Data

Primary source

Survey

Secondary sources

Websites

Articles

Various banking journals

Other published materials

Target population

We have targeted clients of call center, franchise, bank and financial service providers

within Dhaka in march2012.

Sampling Technique

A probability simple random sampling technique has followed in which the sample

elements were randomly selected.

Sampling size

Data collected from 150 respondents from call center, franchise, bank and financial

service providers.

Measurement and Scaling

9-point Likert scale questions have been constructed in the questionnaire.

8

Questionnaire Development

A structured questionnaire has used to collect data. The questionnaire has been developed

in a way that divulges the respondent’s response related to each of the construct. The

questionnaire has been formed on 9-point likert Scale to measure the degree of perception

of respondents on each variable. The respondents were asked to rate statements based on

their perception and opinion from 1 to 9 points.

Data collection/Field Work

We have conduct field work in terms of the guidelines presented in the chapter 13 of the

textbook. We, the member of the group, equally conducted field work. We divided the

total respondent and then we have carried out the field work.

Data Analysis and Result

The data is analyzed by the models of Structural Equation Modeling of confirmatory

factor analysis by AMOS 18 software.

Goodness of fit measure for the measurement model

Minimum was achieved means Amos reached a local minimum

Chi-square = 225.884(value is high and have positive effect)

Degrees of freedom = 109 (higher value is good fit measure)

Probability level = .000 (significant at .05 level because it is .05)

RMSEA (Root Means Square Error of Approximation) is .085 for the default

model and .12d for independent model both are ≥.08 highlighting badness of fit

measures

NFI (Normed Fit Index) is .549 for the default model, 1.00 for the saturated

model and .000 for independent model. The average NFI is .90 that means the

model is not in good ness of fit situation.

CFI (Comparative Fit Index) is .664 for the default model, 1.00 for the saturated

9

model and .000 for independent model. The average CFI is .90 that means the

model is not in good ness of fit situation.

The model is recursive

The model is recursive that means if the structural model would it contained no dual

dependencies or feedback loop.

Model contains the following variables

All variables in the model are listed here, classified as observed or unobserved, and as

either endogenous or exogenous. A summary table shows the number of variables in each

category, as well as the total number of variables in the model.

Spelling or typing errors in the input file can usually be detected by inspecting this

display, since variant spellings of a variable name are interpreted as names for distinct

variables.

Observed, endogenous variables Unobserved, exogenous variables

tan3

tan2

tan1

rel5

rel4

rel3

rel2

rel1

res3

Tan

e3

e2

e1

Rel

e8

e7

e6

e5

10

res2

res1

ass3

ass2

ass1

emp3

emp2

emp1

e4

Res

e11

e10

e9

Ass

e14

e13

e12

Emp

e17

e16

e15

Number of variables in model: 39

Number of observed variables: 17

Number of unobserved variables: 22

Number of exogenous variables: 22

Number of endogenous variables: 17

Limitation of the Analysis and the study

The findings of this study can be generalized after taking into consideration following

limitations:

We found problem with analysis of the structural model

The badness of fit measure SRMR is not found

The all goodness and badness of fit measure for structural model is not calculated

Sample size: A small number of respondents (150) from Dhaka city have been

used in this study. The respondents were selected only from the educational

institutions. So, the samples may not represent the population of the country.

11

Time & Finance: We have got only two week to collect data, input data, analysis

data, and to prepare final report. This is relative small time to conduct research on

this big topic. We had to collect data only from Dhaka city due to financial

constraint.

Errors: We know there are mainly two types of errors – random sampling error,

and non-response error. We selected simple random sampling technique to select

respondents. But, few respondents were interviewed based on the convenience.

Besides, there is some questioning error involved in this project due to

inexperience and lack of comprehension of the interviewers. Some respondents

were unwilling to give certain information. Therefore, we had to probe to get the

information. But it seemed the information was not exact.

Conclusion and Opportunities for Further Study

Service quality dimensions are not defined properly and the structural equation modeling

identified the undefined variables and their effect on the service level and ultimate

customer satisfaction. There is ample of opportunity for using structural model to identify

the significance of the each dimension with their attributes contribution to each of the

service industry. These can be prioritizing by their factor loading. Each of the attributes

for the constructs can be helpful for the marketing implication in all of the mix and other

marketing decisions.

Reference

12

Malhotra N. K. and Dash S. 2010. Marketing Research: An Applied Orientation,

published by Pearson Education. Inc.,Prentice Hall Cop.2010

Parasuraman, A., V. Zeithaml and L. Berry 1985. A conceptual model of service quality

and its implications for future research. Journal of Marketing. 49(4). 41–50.

Asubonten, P., K. J. McCleary and J. E. Swan 1996. SERVQUAL revisited: A Critical

Review of Service Quality. The Journal of Services Marketing. 10(6). 62.

Boulding, W., A. Kalra, R. Staelin and V. Zeithaml 1993. A Dynamic Process Model of

Service Quality: From Expectations to Behavioral Intentions. Journal of Marketing

Research. 30(1): 7–27.

Brady, M. K. and J. Cronin Jr. 2001. Some new thoughts on conceptualizing perceived

service quality: A hierarchical approach. Journal of Marketing. 65(3): 34–49.

Brady, M. K., G. A. Knight, J. J. Cronin Jr., G. Tomas, M. Hult and B. D. Keillor

2005.Removing the contextual lens: A multinational, multi-setting comparison of service

evaluation models. Journal of Retailing. 81(3): 215–230.

Buttle, F. 1996. SERVQUAL: Review, critique, research agenda. European Journal of

Marketing. 30(1): 8–32.

Carman, J. M. 1990. Consumer Perceptions of Service Quality: An Assessment of the

SERVQUAL Dimensions. Journal of Retailing. 66(1): 33–55.

Cronin, J. J. and S. A. Taylor 1992. Measuring Service Quality: A Reexamination and

Extension. Journal of Marketing. 56(3): 55–68.

Fornell, C., M. D. Johnson, E. W. Anderson, J. Cha and B. E. Bryant 1996. The

American Customer Satisfaction Index: Nature, Purpose and Findings. Journal

of Marketing. 60(4): 7–18.

Grönroos, C. 1983. Strategic Management and Marketing in the Service Sector.

Marketing Science Institute. Boston, MA.

Grönroos, C. 1984. A Service Quality Model and Its Marketing Implications. European

Journal of Marketing. 18(4): 36–44.

Lehtinen, U. and J.R. Lehtinen 1982. Service quality: a study of quality dimensions.

Working Paper. Service Management Institute. Helsinki.

O’Connor, S. J., R. M. Shewchuk and L. W. Carney 1994. The Great Gap. Journal of

13

Health Care Marketing 14(2): 32–39.

Oliver, R. L. 1980. A Cognitive Model of the Antecedents and Consequences of

Satisfaction Decisions. Journal of Marketing Research. 17(4): 460–490.

Parasuraman, A. 1998. Customer service in business-to-business markets: an agenda for

research. The Journal of Business & Industrial Marketing. 13(4): 309.

Parasuraman, A. and V. Zeithaml 2006. Understanding and Improving Service Quality: A

Literature Review and Research Agenda. In B. Weitz and R. Wensley (Ed.), Handbook of

Marketing. London: Sage Publications.

Parasuraman, A., V. Zeithaml and L. Berry 1985. A conceptual model of service quality

and its implications for future research. Journal of Marketing. 49(4). 41–50.

Parasuraman, A., V. Zeithaml and L. Berry 1988. SERVQUAL: A multiple-item scale

for measuring consumer perceptions of service quality. Journal of Retailing. 64(Spring).

12–37.

Parasuraman, A., V. Zeithaml and L. Berry 1991. Refinement and reassessment of the

SERVQUAL scale. Journal of Retailing. 67(4). 420–450.

Parasuraman, A., V. Zeithaml and L. Berry 1994. Reassessment of expectations as a

comparison standard in measuring service quality: implications for future research.

Journal of Marketing. 58(1). 111–124.

Peter, P. J., G. A. Churchill and T. J. Brown 1993. Caution in the use of difference scores

in consumer research. Journal of Consumer Research. 19(March): 655–662.

Richard M. D. and A. W. Allaway 1993. Service Quality Attributes and Choice

Behavior.The Journal of Services Marketing. 7(1): 59–68.

Woo, K. and C. T. Ennew 2005. Measuring business-to-business professional

servicequality and its consequences. Journal of Business Research. 58: 1178–1185.

Questionnaire

14

1. Up to date equipment is essential-1.Extremely

Disagree2.Strongl

yDisagree

3.Disagree 4.Somewhat

Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree

7.Agree 8.StronglyAgree

9.Extremely Agree

- - - - - - - - -

2. Physical facility covers much attraction and activity1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

3. Service Provider must be well groomed1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

4. Service provider promises to do is must1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

5. Service provider shows sincere interest1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

6. Service provider is dependable1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

15

7. Service provider performs the service right at the first time1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

8. Service provider tells me exactly when service will be performed1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

9. Customer service staffs give me prompt services1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

10.Customer service staffs are ready to respond to customer requests1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

11.Customer service staffs have knowledge to answer customer question1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

12.I feel safe in the transaction with the service provider1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

16

13.I can trust the service provider's customer service staffs1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

14.The billing system is trustworthy1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

15.Customer service staffs are polite1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

16.Service provider gives customer individual attention1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -

17.Customer service staffs understand customer specific needs1.Extremely

Disagree2.Strongl

yDisagree


Disagree

5.Neither Agree

nor Disagree

6.Somewhat

Agree


9.Extremely Agree

- - - - - - - - -Appendices

Probability level = xxxxx

If the appropriate distributional assumptions are met and if the specified model is

correct, then the value xxxxx is the approximate probability of getting a chi-square

17

statistic as large as the chi-square statistic obtained from the current set of data. For

example, if xxxxx is .05 or less, the departure of the data from the model is

significant at the .05 level.

The appropriateness of hypothesis testing in model fitting, even when the necessary

distributional assumptions are met, is routinely questioned (e.g., Bollen & Long,

1993).

Notes for Group (Group number 1)

Notes that refer to a single group

Messages that relate to a single group appear here. For example, a group's sample

size is reported here.

Sample size = 150

The model is recursive

In everyday usage, a recursive model is one in which no variable in the model has an

effect on itself. That is, in the path diagram of the model, it is not possible to start at

any variable and, by following a path of single-headed arrows, return to the same

variable.

Variable counts (Group number 1)

Weights Covariances Variances Means Intercepts Total

Fixed 22 0 0 0 0 22

Labeled 0 0 0 0 0 0

Unlabele

d12 10 22 0 17 61

Total 34 10 22 0 17 83

18

Notes for Model (Group number 1 - Default model)

Notes that refer to a single model

Messages that relate to a single model appear here. For example, the message

"Minimum was achieved" is displayed here when a model was fitted successfully.

The following covariance matrix is not positive definite (Group number 1 - Default

model)

Amos can produce estimates of variances and covariances that yield covariance

matrices that are not positive definite (Wothke, 1993). Such a solution is said to be

inadmissible. Amos does not attempt to distinguish between a solution that is outside

the admissible region and one that is on or near its boundary.

Testing structural equation models we conclude the decision is: "This solution is not

admissible".

Standardized Regression Weights: (Group number 1 - Default model)

Estimate

tan3<--

-Tan .676

tan2<--

-Tan .874

tan1<--

-Tan .761

rel5<--

-Rel .702

rel4<--

-Rel .458

rel3<--

-Rel .428

19

Estimate

rel2<--

-Rel .575

rel1<--

-Rel .679

res3<--

-Res .807

res2<--

-Res .682

res1<--

-Res .217

ass3<--

-Ass .562

ass2<--

-Ass .844

ass1<--

-Ass .820

emp3<--

-Emp .541

emp2<--

-Emp .763

emp1<--

-Emp .182

When Tan goes up by 1 standard deviation, tan3 goes up by 0.676 standard

deviations.

Correlations: (Group number 1 - Default model)

20

Estimate

Tan <--> Rel .413

Tan <--> Res .120

Tan <--> Ass -.181

Emp <--> Tan .031

Rel <--> Res .932

Rel <--> Ass .727

Emp <--> Rel .784

Res <--> Ass .874

Emp <--> Res .404

Emp <--> Ass .629

All Implied Correlations - Estimates

The correlation matrix displayed here is an estimate of the population correlation

matrix of all the variables in the model (observed and unobserved) under the

hypothesis that the model is correct.

A

ss

R

es

R

el

Ta

n

E

m

p

e

m

p1

e

m

p2

e

m

p3

as

s1

as

s2

as

s3

re

s1

re

s2

re

s3

rel

1

rel

2

rel

3

rel

4

rel

5

ta

n1

ta

n2

ta

n3

A

ss

1.

00

0

R

es

.8

74

1.

00

0

R

el

.7

27

.9

32

1.

00

0

Ta

n

-.

18

1

.1

20

.4

13

1.

00

0

E

m

p

.6

29

.4

04

.7

84

.0

31

1.

00

0

e

m

p1

.1

15

.0

74

.1

43

.0

06

.1

82

1.

00

0

e

m

p2

.4

80

.3

09

.5

98

.0

24

.7

63

.1

39

1.

00

0

e

m

.3 .2 .4 .0 .5 .0 .4 1.

00

21

A

ss

R

es

R

el

Ta

n

E

m

p

e

m

p1

e

m

p2

e

m

p3

as

s1

as

s2

as

s3

re

s1

re

s2

re

s3

rel

1

rel

2

rel

3

rel

4

rel

5

ta

n1

ta

n2

ta

n3

p3 40 19 24 17 41 99 13 0

as

s1

.8

20

.7

16

.5

96

-.

14

9

.5

16

.0

94

.3

93

.2

79

1.

00

0

as

s2

.8

44

.7

37

.6

14

-.

15

3

.5

31

.0

97

.4

05

.2

87

.6

92

1.

00

0

as

s3

.5

62

.4

91

.4

09

-.

10

2

.3

54

.0

64

.2

70

.1

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1.

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0

Standardized Total Effects - Estimates

The total effect of each column variable on each row variable after standardizing all

variables.

Ass Res Rel Tan Emp

emp

1.000 .000 .000 .000 .182

emp .000 .000 .000 .000 .763

22

Ass Res Rel Tan Emp

2

emp

3.000 .000 .000 .000 .541

ass1 .820 .000 .000 .000 .000

ass2 .844 .000 .000 .000 .000

ass3 .562 .000 .000 .000 .000

res1 .000 .217 .000 .000 .000

res2 .000 .682 .000 .000 .000

res3 .000 .807 .000 .000 .000

rel1 .000 .000 .679 .000 .000

rel2 .000 .000 .575 .000 .000

rel3 .000 .000 .428 .000 .000

rel4 .000 .000 .458 .000 .000

rel5 .000 .000 .702 .000 .000

tan1 .000 .000 .000 .761 .000

tan2 .000 .000 .000 .874 .000

tan3 .000 .000 .000 .676 .000

Among the variables of each construct one is important to another. In case of tangibility,

tan2 meaning physical facility contributes much variation.

Measuring model fit through Reliability and Validity

23

CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 61 225.884 109 .000 2.072

Saturated model 170 .000 0

Independence

model17 501.155 153 .000 3.276

Baseline Comparisons

Model

NFI

Delta

1

RFI

rho1

IFI

Delta2

TLI

rho

2

CFI

Default model .549 .367 .702 .529 .664

Saturated model 1.000 1.000 1.000

Independence model .000 .000 .000 .000 .000

Parsimony-Adjusted Measures

ModelPRATI

OPNFI PCFI

Default model .712 .391 .473

Saturated model .000 .000 .000

Independence model 1.000 .000 .000

RMSEA

ModelRMSE

ALO 90 HI 90 PCLOSE

Default model .085 .069 .100 .000

24

ModelRMSE

ALO 90 HI 90 PCLOSE

Independence model .124 .112 .136 .000

25

Sem in Servqual

Documents

structural

confirmatory

financial

extre mely

customer service

improving

measuring

total service