An Application of Neural Network to Service Quality
King-Jang Yang(楊錦章)1, Chin-Chow Yang(楊錦洲)2 and Bai-Sheng Chen(陳百盛)2
1
Department of Applied Mathematics, Chung Hua University
No. 707, Sec. 2, Wufu Rd., Hsinchu, 30012, Taiwan, R.O.C.
Tel: 03-518-6388, Fax: 03-518-6435
Email: kingjang@chu.edu.tw
2
Department of Industrial Engineering, Chung Yuan Christian University
No. 200, Chung Pei Rd., Chung Li, 32023, Taiwan, R.O.C.
Tel: 03-265-4405, Fax: 03-265-4499
Email: chinchow@cycu.edu.tw
Abstract
In this paper, we present a classification model of neural network to measure the performance of service
quality for a system and service certification provider.
In order to demonstrate the validity of our model, we
also use the case study to build a neural network model using the backpropagation learning algorithm, and
compare its classification performance against the linear discriminant analysis. The result shows that
backpropagation neural network model is superior to linear discriminant analysis model.
Keywords: neural network, service quality, linear discriminant analysis.
1. Introduction
attributes for customers as they assess the quality of
When many organizations try to find the ways to
service.
In this study, we find out the perspective of
increase the customers’ satisfaction and loyalty,
importance and satisfaction using questionnaire
service quality management has become one of the
survey to represent the expectations and perceptions
most critical aspects in organizational control.
respectively for customers in performance of service
As a
result, managers have faced deep pressure to measure
quality.
the performance of service quality. However, service
This research constructs a measurement model
quality is an abstract and elusive thing in construct
of importance and satisfaction in service quality from
because of its three particular features in service
the view of customers.
delivery, including intangibility, heterogeneity and
data mining technology to discover the business
inseparability.
problems is another topic we concern.
Hence, it is difficult to assess the
Moreover, how to utilize the
Using the
service quality objectively. Basically, the service
technique of neural network to solve these problems
quality measurement depends on the customers’
is more popular than traditional methods.
perceptions of the performance on service process.
difference between neural networks and other
There always exists some gap or difference between
statistical methods is that neural networks have no
the
actual
assumptions about the statistical distribution or
perceptions in service quality. The key point is to
properties of the data, and therefore tend to be more
understand
useful in practical situations [5]. The classification
customers’
the
expectations
importance
and
their
of various quality
The main
characteristic of neural network was used to be the
to [1]. More recent applications of SERVQUAL can
data mining tool to fulfill the measurement model of
be found in [4, 8].
service quality.
In this study, we use a practical
In
order
to
improve
and
maintain
the
example of a system and service certification
relationship between organization and customer, how
provider to demonstrate the advantage of neural
to discover the special patterns or attributes from the
network approach for service quality measurement.
customers’ database is the key, and data mining plays
First of all, we integrate some important
the
important
role
customer
mining,
also
relationship
attributes of service quality from the viewpoint of
management.
customers to understand their expectations.
“knowledge discovery in database”, is the process of
The
Data
in
objective of first stage is to extract several critical
discovering
criteria of service quality.
databases that is useful for decision making.
Overall survey is the
meaningful pattern
known
as
from customer
Berry
second stage. According to the results of first stage,
and Linoff [7] define data mining as the exploration
29 criteria are divided into six dominant dimensions
and analysis, by automatic or semiautomatic means,
by using factor analysis.
of large quantities of data in order to discover
Accordingly, these six
factors fed as the input variables to build a
meaningful patterns and rules.
backpropagation neural network (BPN) model.
To
of data mining in practical applications include
evaluate the effectiveness and efficiency of the
marker basket analysis, memory based reasoning,
proposal neural network classification model, it is
cluster detection, link analysis, decision tree, rule
compared with linear discriminant analysis (LDA)
induction, neural network, and genetic algorithms, etc.
with respect to six key factors.
[9].
The result shows
that the classification performance of neural network
Specific techniques
In these methods, neural network approaches are
can appropriately discriminate customer categories
becoming increasingly popular in business.
A
on the basis of those six factors.
Besides, on the
survey of journal articles on business applications
accuracy of classification, the BPN model is superior
published between 1988 and 1995 indicates that an
to LDA model.
Finally on the considerations of
increasing amount of neural network research is
balancing customers’ perceptions and expectations,
being conducted for a diverse range of business
the neural network model can provide information to
activities [2].
take decision for improvement.
solutions for business problems using the techniques
Many organizations are going to find
of neural network and data mining, those tasks are
2. Literature Review
typically being the domain of the operations
Service quality is often difficult to assess
because of its subjective nature from customers. The
measurement model of the service quality construct
has been dominated by the use of the SERVQUAL
scale developed by Parasuraman et al. (1988). This
measurement model proposes a five gap-based
comparison
between
the
expectations
performance perceptions of customers.
and
For more
information on SERVQUAL approaches please refer
researcher, like forecasting, modeling, clustering, and
classification [5].
According to
the
widely
applications of neural network, we present a
classification model to evaluate the performance of
service quality for a system and service certification
provider.
We furthermore build a similar model
using the backpropagation learning algorithm and
compare its classification performance against the
linear dscriminant analysis.
performance through the pretest questionnaire, and
3. Research Methodology
revised the unclear and uncertain expressions of
There are five major steps for the proposed
neural network approach as shown in Figure 1.
measurement items.
In
the following section, we use the sample data set of
(3)
Importance and satisfaction survey: In this step,
surveillance audit process for a system and service
we design a concise multiple item scales that contain
certification provider as the empirical illustration.
29 pairs of Likert-type items, where each item is
recast into two statements.
External customers
Internal customers
One half of these items
are intended to measure the important degree of
customers’ expectations about the service categories
being investigated, and the other 27 matching items
Critical to service quality attributes
are intended to measure the satisfactory degree of
their preceptions about the service performance.
The items are presented in a five-point response
Importance survey
Satisfaction survey
Factor analysis
format with the degrees from strongly important
(satisfactory)
to
strongly
unimportant
(dissatisfactory).
Service quality is measured by
calculating the ”difference scores” between these
corresponding items, that is the difference between
Neural network modeling
customers’ expectative importance and perceptive
Service quality classfication model
Figure 1 Implementation process of proposed model
satisfaction on the quality attributes.
(4)
Factor analysis:
In order to reduce the
numbers of items (variables) , we used the principal
(1)
Interview of external and internal customers:
How the customers feel on the important quality
attributes is to be considered as the dimensions of the
auditors’
capability,
organizational
policies,
administration, and certificate issuance when the
certification company processes surveillance audit.
During the research period, we had interviewed
twenty important customers, and had conducted two
panel discussions by using nominal group technology
with the front-line servers, in order to determine the
important quality attributes and to design the valid
questionnaire .
(2)
Critical to service quality attributes: We
selected the variable which were identified to have
more than 80 % of significant influence on service
component analysis of principal axes method with the
varimax criterion of orthogonal rotation to perform a
factor analysis.
Besides, there is no theoretical
method to determine the best-input variables of the
designed
neural
network
model.
Hence,
this
procedure can be performed as a generally method to
determine the number of a good subset of input
variables.
In this procedure, we select the variables
which factor loading is greater than 0.45, and
discover linearly related variables and regroup them
into a compound factor.
These compound factors
will be the input nodes in the next step of neural
network modeling .
(5)
Neural
network
modeling:
A
supervised
learning algorithm of backpropagation is utilized to
establish the neural network modeling. A normal
scores” was used to perform the factor analysis with
backpropagation neural (BPN) model consists of an
the principal component estimation method and the
input layer, one or more hidden layers, and an output
varimax
layer. There are two parameters including learning
Furthermore, the original set of 29 variables was
rate 0    1 and momentum ( 0    1 ) required
reduced to 6 principal compounded factors, where the
to define by the user. The theoretical results showed
55.25% of variance was explained. The regrouping
that one hidden layer is sufficient for a BP network to
measurement variables can be set into six different
approximate any continuous mapping from the input
dimensions included responsiveness ( x1 ), assurance
patterns to the output patterns to an arbitrary degree
( x2 ), reliability ( x3 ), empathy ( x4 ), value-added
freedom[6].
The selections and combinations of
service ( x5 ), and tangibility ( x6 ) included 7,5,5,4,3
learning rate, momentum and the nodes of hidden
and 3 items respectively. The next step was to assess
layers primarily affect the classification performance.
the performance of the services provider, which was
rotation
with
Kaiser
normalization.
to be explained by these six critical factors.
4. Empirical Illustration
Also,
these six factors were used to be the input variables in
4.1 Purpose of study: The objective of this study is
the next step of neural network modeling .
to classify the performance of service quality on the
considerations of auditors’ capability, organizational
4.4 Neural network modeling: The 467 respondent
policies, administration, and certificate issuance for a
firms were randomly separated into two groups,
system and service certification provider located in
namely, 75% for training patterns and 25% for testing
Taipei, Taiwan. We furthermore make the comparison
patterns.
of classification performance between the BPN
momentum and the number of nodes in the hidden
model and LDA model.
layer should be defined for backpropagation network
The three parameters of learning rate,
modeling. In the training model, the six factors were
4.2 Sample: The data for this study were collected
fed as input nodes as discussed in section 4.3.
from 529 firms that responded to a mailed survey.
the output nodes decision, we then divided the
The responding firms were primarily involved 110
sample into three categories of high, medium and low
firms of High-tech industry, 51 firms of traditional
service quality for services provider’s performance
manufacturing, 54 firms of construction industry, 116
based on customers’ judgments.
firms of machinery and equipment, 73 firms of
range of 0.6-0.9 and 0.1-0.4 to be the decisions of
chemical and plastic products, 88 firms of service
learning rate and momentum [3].
industry, 26 firms of others and 11 firms missed.
rule-of-thumb for number of hidden nodes defined as
The response rate based on 529 returns was 25.68 %.
h 5   m  n  , where h , m , and n represent the
There were 467 valid samples to perform the
number of training patterns, output nodes and input
following analysis, and the valid rate of samples was
nodes respectively. Then the root-mean-square error
22.67%.
(RMSE) and classification rate are the measurement
For the reliability test, the alpha values of
importance,
satisfaction
and
difference
scores
evaluation are all greater than 0.92.

For
We adopted the
A very rough

indictors to validate the performance of the training
model. Through several trial-and-error experiments,
the structure of 6-9-3 model had the best performance.
4.3 Factor analysis: In this step, the “difference
Furthermore, five nodes for the second hidden layer
had validated to be the highest classification rate and
F  0.645 , it is ranked to be medium, and if
lowest RMSE. In order to obtain the optimal
F  0.645 , it is ranked to be low.
combination of learning rate and momentum, it then
model classifies correctly in 71.73% of the sample.
used sixteen combinations settings as: (0.6, 0.1), (0.6,
The classification rate of low service quality is only
0.2), (0.6, 0.3), (0.6, 0.4),…, (0.9, 0.4).
43.80%, it is clear that the LDA model is not good
In terms of
The LDA
learning rate and momentum, the best setup is (0.6,
enough for classification.
0.3) on the considerations of classification rate and
the classification rates of training and testing are
RMSE.
94.40% and 87.71% separately.
The relationship between the RMSE and
For the best BPN model,
And the RMSEs of
the numbers of learning iterations for the selected
training and testing are 0.1174 and 0.1516 separately.
6-9-5-3 model is in Figure 2.
The BPN model has 85.86% network accuracy in
classifying service quality categories.
From the
classification results of accuracy, the BPN model is
0.24
0.22
superior to LDA model on all three categories and
RMSE
0.2
total accuracy is shown in Table 1.
0.18
0.16
Therefore, the
backpropagation neural network model has been
0.14
demonstrated as a good method because it predict
0.12
5000
15000
30000
50000
70000
90000
Iterations
Figure 2 RMSE versus numbers of learning iterations
4.5 Neural network and linear discriminant
analysis: To validate the efficiency of the proposed
BPN model for the practical application, it is
compared with linear discriminant analysis (LDA)
well in classification problem.
Table 1 Classification results of the two models
BPN
LDA
High service quality
88.09%
66.67%
Medium service quality
84.21%
82.31%
Low service quality
85.29%
43.80%
Total accuracy
85.86%
71.73%
Choosing one specific improvement strategy
with respect to six critical factors to obtain a linear
over another is difficult for managers.
model.
the obligation for managers to do that, since
This model is a linear combination of
responsiveness,
assurance,
reliability,
But it is also
empathy,
improvement strategy can make the allocation of
value-added service and tangibility factors to separate
resources more effective under organizational limited
the service quality into three groups.
resources.
The linear
To
obtain
further
improvement
discriminant functions by using stepwise regression
information, we suggest using statistical analysis to
method for the high ( F1 ), medium ( F2 ) and low ( F3 )
extract the data from the low service category.
service quality are as following:
study of this category should provide some insight
A
F1  4.616  4.073x1  2.785x3  2.814 x4  1.11x6
for managers to make decision about improvement
F2  1.487+0.457x1 +1.144x3 +0.343x4 1.331x6
strategy.
F3  3.471  1.393x1  2.553x3  1.289 x4  0.359 x6
The threshold value is 5.563 , meaning that if
5. Conclusion
the discriminant function F  5.563 , then the
The main result of this research are twofold.
service level on the service performance of the
The first is that we illustrate the procedures of service
customer perceived is ranked to be high. If
quality measurement from the customers’ perspective.
Our proposal model could be used to predict service
quality classifications by applying neural network
reasonably well.
The second result shows that the
New Jersey: Prentice -Hall, 1994.
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classification performance of backpropagation neural
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The backpropagation neural network is an
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M. K. Brady, J. J. Cronin and R. R. Brand,
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“Performance-only measurement of service
volumes of uncertain information from customers’
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Business Research, vol. 55, ppl7-31, 2002.
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can
be
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construct
optimal
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