Sem in Servqual
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Transcript of 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
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
3. Service Provider must be well groomed1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
4. Service provider promises to do is must1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
5. Service provider shows sincere interest1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
6. Service provider is dependable1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
15
7. Service provider performs the service right at the first time1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
8. Service provider tells me exactly when service will be performed1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
9. Customer service staffs give me prompt services1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
10.Customer service staffs are ready to respond to customer requests1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
11.Customer service staffs have knowledge to answer customer question1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
12.I feel safe in the transaction with the service provider1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
16
13.I can trust the service provider's customer service staffs1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
14.The billing system is trustworthy1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
15.Customer service staffs are polite1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
16.Service provider gives customer individual attention1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
9.Extremely Agree
- - - - - - - - -
17.Customer service staffs understand customer specific needs1.Extremely
Disagree2.Strongl
yDisagree
3.Disagree 4.Somewhat
Disagree
5.Neither Agree
nor Disagree
6.Somewhat
Agree
7.Agree 8.StronglyAgree
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
91
.4
61
.4
75
1.
00
0
re
s1
.1
89
.2
17
.2
02
.0
26
.0
88
.0
16
.0
67
.0
47
.1
55
.1
60
.1
07
1.
00
0
re
s2
.5
96
.6
82
.6
35
.0
82
.2
76
.0
50
.2
10
.1
49
.4
88
.5
03
.3
35
.1
48
1.
00
0
re
s3
.7
05
.8
07
.7
52
.0
96
.3
26
.0
59
.2
49
.1
77
.5
78
.5
95
.3
96
.1
75
.5
50
1.
00
0
rel
1
.4
94
.6
33
.6
79
.2
80
.5
32
.0
97
.4
06
.2
88
.4
05
.4
17
.2
78
.1
37
.4
32
.5
11
1.
00
0
rel
2
.4
18
.5
36
.5
75
.2
37
.4
51
.0
82
.3
44
.2
44
.3
43
.3
53
.2
35
.1
16
.3
65
.4
32
.3
91
1.
00
0
rel
3
.3
11
.3
99
.4
28
.1
77
.3
36
.0
61
.2
56
.1
82
.2
55
.2
63
.1
75
.0
87
.2
72
.3
22
.2
91
.2
46
1.
00
0
rel
4
.3
33
.4
26
.4
58
.1
89
.3
59
.0
65
.2
74
.1
94
.2
73
.2
81
.1
87
.0
92
.2
91
.3
44
.3
11
.2
63
.1
96
1.
00
0
rel
5
.5
11
.6
55
.7
02
.2
90
.5
51
.1
00
.4
20
.2
98
.4
19
.4
31
.2
87
.1
42
.4
46
.5
28
.4
77
.4
04
.3
01
.3
21
1.
00
0
ta
n1
-.
13
8
.0
91
.3
14
.7
61
.0
24
.0
04
.0
18
.0
13
-.
11
3
-.
11
7
-.
07
8
.0
20
.0
62
.0
73
.2
13
.1
81
.1
34
.1
44
.2
21
1.
00
0
ta
n2
-.
15
9
.1
04
.3
61
.8
74
.0
27
.0
05
.0
21
.0
15
-.
13
0
-.
13
4
-.
08
9
.0
23
.0
71
.0
84
.2
45
.2
07
.1
54
.1
65
.2
53
.6
64
1.
00
0
ta
n3
-.
12
3
.0
81
.2
79
.6
76
.0
21
.0
04
.0
16
.0
11
-.
10
1
-.
10
3
-.
06
9
.0
18
.0
55
.0
65
.1
89
.1
60
.1
19
.1
28
.1
96
.5
14
.5
90
1.
00
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