Proceedings of Applied International Business Conference 2008
THE EVALUATION OF CUSTOM SERVICE QUALITY BY FUZZY SERVQUAL
M. Ali Abdolvand, Abbas Toloie and M.Javad Taghipouryan ψ
Islamic Azad University, Iran
___________________________________________________________________________________
Abstract
Increased service quality is believed to have a positive effect on customer loyalty and Profitability in
the contemporary service industry. Accurate measurement of service quality is a major concern to
management. Whereas current service quality evaluation methods for evaluating services are generally
decisive at time those criteria of measurement are fuzzy, ambiguous and verbal, Appling fuzzy method
according to other methods are closet to human thinking. Therefore, purpose of this paper is evaluating
five dimensions of service quality in SERVQUAL model by using fuzzy logic that we call Fuzzy
SERVQUAL. For this purpose, from Caspian sea margin customs, 3 customs have been chosen and a
sample including 194 clients (exporters, importers, commission agents, ……) have been determined
that more than testing this new method, we pay attention to rank of each diminution of five whole
diminutions of service quality and comparing expectation of clients and custom performance by using
fuzzy logic.
___________________________________________________________________________________
Keywords: Fuzzy logic; SERVQUAL; Custom service quality; Caspian Sea.
JEL Classification Codes: M3; M10.
1. Introduction
Nowadays, service is one of the main components in the world economics and according to the WTO’s
evaluation in 2003, value of commercial services is about 1.8 trillion$ (Kerin et al., 2005). In other side,
governments have important role to comply major services and offer main services through courts,
hospitals, post offices and so fort (Arasli, 2008).
Custom in the international term is a state – service organization and like other state organization is an
executor of responsibilities in the rule and regulation framework that range of its responsibilities has
certain expansiveness and its goal in the respect of marketing is satisfaction of impersonal needs. Rate
of exportable goods value in 2004 as compared with 2003 have been increased about 53.3% and
importable goods have been increased about 21.1precent (Islamic Republic of Iran’s Customs
Administration, available at www.irica.gov.ir). Increased service quality is believed to have positive
impact on customer loyalty and profitability (Magi and Julander, 1996) so accurate measurement of
service quality is a major concern to management (Liou and Chen, 2006).
It is along time that behavioral science scientists and quality theorist have understand, there is
ambiguity in a lot of humans judgment. in 1965, an Iranian professor in Colombia University (Zadeh,
1965) introduced Fuzzy set in humans systems (systems with human interaction such as services
systems) and decision procedure , as a tool against ambiguity and imprecise(Abbott, 1996). However
measuring criteria of services quality and satisfaction are fuzzy and ambiguous but available methods
measuring them generally are classic kind (Liou and Chen, 2006).
Therefore ¸ purpose of this paper is evaluating five dimensions of service quality in SERVQUAL
model by using fuzzy logic that we call Fuzzy SERVQUAL. For this purpose ¸ from Caspian sea
margin customs ¸ 3 customs have been chosen and a sample including 194 clients (exporters¸
importers¸ commission agents ¸ ……) have been determined that more than testing this new method¸
we pay attention to rank of each diminution of five whole diminutions of service quality and comparing
expectation of clients and custom performance by using fuzzy logic.
ψ
Corresponding author. M. Javad Taghipouryan. Department of Business Management, Science and
Research Branch, Islamic Azad University, Tehran, Iran. Corresponding author Email:
[email protected]
Proceedings of Applied International Business Conference 2008
2. Theoretical framework
SQ and SERVQUAL
Service quality (SQ) is considered a critical determinant of competitiveness. Service quality can help
an organization to differentiate itself from other organizations and gain a competitive advantage.
Superior service quality is a key to improved profitability (Ghobadian et al., 1994). The intangible
nature of services has obstructed the advancement of the field of service quality. Whereas
manufactured products are amenable to sampling, gauging and measurement of various types, services
are less so (Jannadi and Al-Saggaf, 2000). Since services are intangible, heterogeneous and inseparable,
it is difficult to measure service quality objectively. Over the years, many researchers have proposed
and evaluated alternative service quality models and instruments for measuring service quality (Zhao et
al., 2002). Among these models, SERVQUAL (Parasuraman et al., 1985) is the most prominent and the
most widely used. Parasuraman et al. (1985) initially developed the SERVQUAL scale. They originally
identified ten service quality factors generic to the service industry, such as tangibles, reliability,
responsiveness, competence, courtesy, credibility, security, access, communication and a willingness to
understand the customer. The main aim at that time was to develop general criteria for measuring
service quality in various service organizations in different sectors. At a later stage, Parasuraman et al.
(1988), developed an instrument and validated it across various service environments, such as higher
education, banks, insurance, tourism, dentistry, health care, communications, credit card services and
car maintenance (Arasli et al., 2005).
The scale’s founders contended that whilst each service-producing industry is unique, there are five
common characteristics, which could be applicable to service organizations:
Table 1: Service quality dimension and their definition (Parasuraman et al., 1988)
Dimension
Definition
Tangibility
Facilities, equipment and the presence of personnel
Reliability
Ability to perform the promised service responsibly and accurately
Responsiveness
Willingness to provide help and a prompt service to
Customers
Assurance
The knowledge and courtesy of employees and their ability to
inspire trust and confidence
Empathy
Caring and understanding, which a company provides and/or offers its customers in
terms of its individualized and personalized attention
This measurement method involves calculation of the differences between consumer expectations and
perceptions (Baggs and Kleiner, 1996). Originally, the SERVQUAL scale contained 22 pairs of items.
Half were aimed at measuring service user expectations and the remaining half measured perceptions.
The seven-point Likert scale is used by some researchers while others use the five-point format (Arasli
et al.,2006).
Fuzzy set theory
‘‘Not very clear’’, ‘‘probably so’’, ‘‘very likely’’, these terms of expression can be heard very often in
daily life, and their commonality is that they are more or less tainted with uncertainty. With different
daily decision making problems of diverse intensity, the results can be misleading if the fuzziness of
human decision-making is not taken into account. However, since Zadeh (1965) was first proposed
fuzzy set theory, and Bellman and Zadeh (1970) described the decision-making method in fuzzy
environments, an increasing number of studies have dealt with uncertain fuzzy problems by applying
fuzzy set theory (Tsuar et al, 2002).
Fuzzy logic provides an inference morphology that enables approximate human reasoning capabilities
to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength
to capture the uncertainties associated with human cognitive processes, such as thinking and reasoning.
Some of the essential characteristics of fuzzy logic relate to the following (Zadeh, 1965):
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Proceedings of Applied International Business Conference 2008
•
•
•
•
•
Exact reasoning is viewed as a limiting case of approximate reasoning;
Everything is a matter of degree;
Knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint
ƒ On a collection of variables;
Inference is viewed as a process of propagation of elastic constraints; and
Any logical system can be fuzzified.
There are two main characteristics of fuzzy systems that give them better performance
for specific applications:
(1) Fuzzy systems are suitable for uncertain or approximate reasoning, especially
for the system with a mathematical model that is difficult to derive; and
(2) Fuzzy logic allows decision-making with estimated values under incomplete or
uncertain information (Kahraman et al.,2007).
Fuzzy set theory has developed as an alternative to ordinary (crisp) set theory and is used to describe
fuzzy sets. For example, the set of 30-year-old men is a crisp set. The boundaries are definite and a
particular person is either in the set or not, is either a 30-year-old man, or is not. In contrast, a fuzzy set
does not have clear boundaries. Membership in a fuzzy set is a matter of degree. For example, what
would one say are the boundaries of the set of warm temperatures? When is a given centigrade
temperature definitely in the set ± at 40o? At 50o? A fuzzy set such as warm temperatures can be
illustrated as in Figure (1). A particular temperature is described by its degree of membership in the set.
Figure A1 shows that 40o is 100 percent a member of the set of warm temperatures, whereas 20o is only
50 percent a member of the set.
Figure 1: Fuzzy set
Figure 2: Crisp set
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Proceedings of Applied International Business Conference 2008
Figure 3: Intersection of two fuzzy sets
This fuzzy set example stands in contrast to a crisp set example, as shown in Figure(2). Figure (2)
shows the crisp set of all temperatures in the 30 o to 40o range. In this set, 30o is 100 percent a member
while 29o is not in the set at all; there is no in-between.
The nature of fuzzy sets allows something to be a member in more than one fuzzy set. For example, a
35-year-old person might be 20 percent a member of the set of young people and 40 percent a member
of the set of middle aged people. Figure A3 shows the overlapping fuzzy sets of warm temperatures
and hot temperatures (Friedlob and Schleifer, 1999).
So, Fuzzy numbers are a fuzzy subset of real numbers, and they represent the expansion of the idea of
confidence interval. According to the definition made by Dubois and Prade (1978), those numbers that
can satisfy these three requirements will then be called fuzzy numbers, and the following is the
explanation for the features and calculation of the triangular fuzzy number (TFN) (Tsuar et al.,2002). If
the membership functions of a fuzzy number A is defined as follows (Liou and Chen, 2006):
 ( x − L1 )
 (M − L )
1
1

 (x − M 1)
A ∆µ A (x) = 
 (M 1 − u1 )
0


L1 ≤ x ≤ M
M
1
1
≤ x ≤ u1
(1)
Other else
According to the nature of TFN and the extension principle put forward by Zadeh (1965), the algebraic
calculation of the triangular fuzzy number(Tsuar et al.,2002).
Addition of triangular fuzzy number :
(L1, M1, U1) (L2, M2, U2) = (L1 + L2 , M1+ M2 , U1+ U2).
(2)
Multiplication of a triangular fuzzy number :
(L1, M1, U1) (L2, M2, U2) = (L1 / L2 , M1/ M2 , U1/ U2).
(3)
Any real number k
K⊗(L, M, U) = (KL, KM, K U)
(4)
Subtraction of a triangular fuzzy number⊖
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Proceedings of Applied International Business Conference 2008
(L1, M1, U1)⊝ (L2, M2, U2) = (L1 - L2 , M1- M2 , U1- U2).
(5)
3. Methodology
Goals and hypotheses
This research has 3 objects including:
• Comparing between expectation of clients and performance of customs in 5 dimensions of
service quality by fuzzy logic;
• Ranking each of 5 dimensions of service quality in two levels, expectation and performance
by fuzzy logic; and
• Measuring rate of custom service quality (CSQ) and obtaining rate of dissatisfaction,
satisfaction and delight of clients by fuzzy logic.
About first object, according to our assumption, there are a significant difference between expectations
of clients and performance of customs that by statistic tests we will examine this assumption in next
sections.
Measures
Questionnaire that applied in this research is extracted from criteria of service quality (SERVQUAL).
In this research, questionnaire have been frame in 3 section: first section related to properties of
population , second section is about importance of each 5 dimension by view point of client and in third
section there are 22 questions about 5 dimensions of service quality for assessing expectations and
perceptions of clients. The aim of this section is presentation of a way for evaluation of CSQ(Custom
Service Quality) by using fuzzy numbers on the basis of five dimensions of service quality in
SERVQUAL model which we call Fuzzy SERVQUAL.
Fuzzy SERVQUAL has 5steps which are consist of :
Step 1- Determination of fuzzy numbers for each of the linguistic variables:
In this study, “strongly disagree” and “strongly agree” five spectrum are used that have been shown as
the following:
Strongly Disagree (SD), (D) Disagree, Middle (M), Agree (A), Strongly Agree (SA). For gaining each
of the linguistic variables’ fuzzy numbers, experts’ opinions were used, so each experts were asked to
determine linguistic variables’ spectrum from 0 to 100. The sample of these opinions is shown in table
(2). Whereas evaluation of custom service quality is in the clients’ view, we chose experts among
clients with at least BA degree and upper and with the intercourse duration of more than 7years with
custom (integration of science and experience) and we could get this determination of spectrum from
30 persons.
Table 2: Scale of linguistic variables by experts
Scale of linguistic variables(0-100)
SD
D
M
A
SA
0 – 20
20 - 40
40 - 60
60 - 80
80 - 100
0 – 10
10 - 30
30 - 50
50 - 70
70 - 100
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
expert 1
expert 2
.
.
.
0 – 20
expert 30
20 - 30
30 - 40
40 - 60
60 - 100
After achieving experts’ opinion by evaluation of these 30 experts in linguistic variables scale, we
determine triangular fuzzy numbers (TFN) of each linguistic variables. According to the above
mentioned, now TFN of each linguistic variables are consist of :
“Strongly Disagree” linguistic variable (SD). So, TFN for SD linguistic variable with membership
function is as the following:
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Proceedings of Applied International Business Conference 2008
L
expert
expert
expert
expert
expert
1
2
3
4
5
expert 30
TFN(SD)
0
0
0
0
0
M=(L+U)/2
10
5
10
5
5
U
20
10
10
10
10
0
0
min
10
8/63
average
20
20
max
Table 3: TFN for SD linguistic variable by using expert’s opinions
Figure 5: Triangular membership function of fuzzy number for “Strongly Disagree”
As it was mentioned, we could obtain TFN for SD linguistic variables by experts’ opinion, and other
linguistic variables’ fuzzy numbers are obtained in this way. These numbers with their membership
function are in the following:
SD = (0,8.63,20) D = (10,24.09,40)
M = (20,38.63,60) A = (30,58.63,80) SA = (60,85.45,100)
Figure 6: Triangular membership function of fuzzy number
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Proceedings of Applied International Business Conference 2008
Step 2 – opinion conversion of each experts who answer the questionnaire according to the obtained
TFN in previous step:
As it was mentioned, data collection tools is a questionnaire based on service quality scale
(SERVQUAL) that operates on the basis of 22 pairs of items relevant to 5dimesion and a comparison
clients expectation and perceptive performance. Since, SERVQUAL is both weighted and nonweighted, of course it should be mentioned that weighted SERVQUAL was used in this research, the
difference between weighted and non-weighted SERVQUAL in fuzzy numbers is shown in table(4).
Table 4: The difference between weighted and non-weighted forms in fuzzy SERVQUAL in data
collection
Level
Non-weighted
fuzzy weighted fuzzy SERVQUAL
First item
SERVQUAL
Answer
TFN
Weight
TFN
(%)
=100),85.45 ,0/9(60
Expectation
A good custom has modern Strongly
100),85.45 ,(60
90
equipments.
agree
90), 76.9,(36
Performance This custom has modern equipments. Agree
80),58.63 ,(30
90
=80),58.63 ,0/9 (30
72),52.76,(27
In this way, for the given answer to each of the 22 pairs of items of questionnaire, we use proper TFN
which was obtained in the previous step.
Step 3- obtaining fuzzy numbers of each 5dimentions of service quality by using fuzzy average:
Whereas the present research has 5dimension by using service quality scale, so after opinion
conversation of each experts who answer to fuzzy numbers in each of the questions, this task should be
done in 5 dimension level and for this reason fuzzy average is used. In this section both weighted and
non-weighted averages have been shown.
Table 5: Five dimension scale of service quality with correspondence of its items
Dimensions
Tangibility Reliability
Responsiveness Assurance
Empathy
Items
correspondence
Number of items
1–4
5-9
10 – 13
14 - 17
18 - 22
4
5
4
4
5
Non-weighted triangular fuzzy average formula is as the following:
(6)
And also weighted triangular fuzzy average formula is as the following:
(7)
In the lower part, fuzzy average for dimension of tangibility in two levels of expectation and
performance and with 2forms of weighted and non-weighted. Other dimensions are obtained in this
way.
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Proceedings of Applied International Business Conference 2008
Weight form
(8)
In which:
E1T = expectations of first responser than dimension of tangibility
P1T = perceptible performance in the view of first responser than dimension of tangibility
W = Importance coefficient of each items in the view of responsers
Non-Weight form
(9)
Step 4 – Defuzzification:
Obtained results in the previous steps for 5dimensions of CSQ have been like TFN that for analysis and
test of hypothesis and comparison between dimensions and decision making should be changed from
triangular number to the crisp number which is called defuzzification.
There are several available methods serve this purpose. Mean-of-Maximum, Center-of-Area, and a-cut
Method are the most common approaches. This study utilizes the Center-of-Area method due to its
simplicity and does not require analyst’s personal judgment (Tsuar et al., 2002).
The defuzzified value of fuzzy number can be obtained from Eq. (10).
A= (L 1,M1 , U1)
NFA= [(U1-L1) + (M1-L1)]/3+ L1
Now in the following part, defuzzification by the way of center of area is for dimensions of tangibility
in two levels of expectations(NFE) and performance(NFP) and other dimensions are also obtained in
this way.
E1T = (L1T ,M1T ,U1T)
NFE1T = [(UE1T – LE1T) + (ME1T – LE1T)]/3 + LE1T
P1T = (L1T ,M1T ,U1T)
NFP1T = [(UP1T – LP1T) + (MP1T – LP1T)]/3 + LP1T
(11)
(12)
Step 5 – Measurement of custom service quality (CSQ):
Whereas in the previous step, we converted fuzzy numbers to the crisp numbers in two levels of
performance and expectation by center of area way. This step for measurement of clients satisfaction,
dissatisfaction and delight of CSQ on basis of SERVQUAL model, is obtained by the following
formula:
NFSQ = NFP – NFE
NFSQ < 0
NFSQ = 0
NFSQ > 0
(13)
clients dissatisfaction
clients satisfaction
clients delight
For determining reliability of this questionnaire from in this research Cronbach¸s ! has been used.
Values of final for each of 5 dimensions of service quality with similar questions are the table 6.
According Sekaran‘s opinion, Cronbach’s coefficient less than 0.6 is weak, 0.7 is acceptable and more
than 0.8 is very good (Amirshahi and Mazhari, 2008).Therefore the, result of this research for 2
dimensions are acceptable and for 3 dimensions are good and whole questionnaire from have good
reliability.
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Proceedings of Applied International Business Conference 2008
Table 6: Custom service quality scores, Cronbach’s alpha
Tangibility
Items
4
Questions
1–4
Cronbach’s Alpha
0.73
Reliability
5
5–9
0.85
Responsiveness
4
10 – 13
0.86
Assurance
4
14 – 17
0.83
Empathy
5
18 – 22
0.78
CSQ
22
1 – 22
0.90
Sample
There are eight customs around Caspian Sea in Iran that we have chosen 3 customs of Mazandaran
province ( Nowshar, Sari, and Amir Abad customs) and 210 questionnaires distributed between clients
( exporters, importers, commission agents and……). Beby (1998) believes 50 percent of answers are
enough for analyzing data and result of reports. Rate of answers for good 60 percent and for very good
is 70 percent (Amirshahi and Mazhari, 2008). In this research, from 210 questionnaires that had been
distributed, 11of them (5.25%) hadn’t been returned , 5 of them (3.28%) weren’t completed and 194 of
them were completed that were ready for analyzing a rate equal with 92.28% that is a good rate.
Table 7: Demographic characteristics of respondents (total number = 194, P= percentages, F=
Frequency)
Sex
P
F
Age
P
F
Occupation
P
F
Intercourse
duration with
custom
Male
96.36% 187 Below
49.48% 96 Exporter
20.12% 39 Below 2
30
Female
3.61%
7
31-40
35.56% 69 Importer
29.89% 58 2-4
41-50
11.34% 22 Commission
40.72% 79 4-7
agent
51 and 3.61%
7
Others
9.27%
18 7 and greater
greater
4. Data analysis and results
The statistical package SPSS (16.0) was used to summarize and analyze the responses. In this research
from nonparametric tests, Wilcoxon signed-rank test for comparing clients expectations and
performance of customs had been chosen that you can see its results in table( 8). The same as you can
see in table 5 in all 5dimensions of service quality, significant amount of test was zero and by %95
certainly can be said that “there is significant difference between clients expectations and performance
of customs in all dimensions”.
Tangibility
Reliability
Responsiveness
Assurance
Empathy
Table 8: Result of Wilcoxon singed – rank test (Total number = 194)
NFSQ < 0 (NFP – NFE) NFSQ > 0 (NFP – NFE) NFSQ = 0
N
Mean Rank
N
Mean Rank
N
155
96.42
32
82.28
7
117
94.87
77
100.28
0
147
96.3
40
85.55
7
140
96.66
43
73.63
11
133
93.29
57
100.65
4
Sig.
0
0
0
0
0
z
-8.92
-2.87
-7.75
-7.09
-5.44
In other side surveying that which of 5dimensions of service quality is in accordance with clients
expectations and performances of customs is allocated to higher level than, Friedman test has been used
that its results has been shown in table 6 in two levels of expectation and performance. Whereas
significant level of test is zero, can be said that between priority of 5dimensions of service quality,
376
P
F
22.16
43
31.96
21.14
62
41
24.73
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Proceedings of Applied International Business Conference 2008
there were a significant difference and have allocated rank 1 to both level of customs performances and
level of clients expectations by its reliability and the last rank belongs to empathy in two levels.
Table 9: Results of Friedman test
Clients expectation
Customs performance
Rank
Mean Rank
Std. Deviation
Mean
CSQ
Dimensions
Mean
Sed. Deviation
Mean Rank
Rank
4
2.69
55.60
233.39
Tangibility
186.25
60.47
2.49
4
1
4.73
61.53
329.5
Reliability
318.46
84.87
4.68
1
3
2.77
55.10
235.62
Responsiveness
200
65.69
2.83
3
2
2.91
57.97
237.33
Assurance
199.66
56.87
2.94
2
5
1.88
74.4
193.40
Empathy
175.34
71.59
2.06
5
The same as has been said in measurement section, rate of custom service quality (CSQ) from view
point of clients can be explained in 3 states that has been shown in Table 10. In 3 states form, can been
said that 49.8 percent of clients, imply dissatisfaction of custom service quality, 28percent are satisfy
and 22.2percent are delight because they found custom performance higher than their expectations and
also if in 2 states form we suppose a state, we should say 50.2 percent are satisfy CSQ and against it,
49.8 percent of them aren’t satisfy.
Table 10: Measure of customs service quality by Fuzzy SERVQUAL
Items
NFSQ < 0
NFSQ = 0
N
%
N
%
24
12.38
139
71.64
1
22
11.35
112
57.73
2
33
17.2
92
47.42
3
Tangibility
30
15.47
127
65.46
4
7
3.6
155
79.9
Total(1- 4)
30
15.46
146
73.19
5
21
10.83
107
55.15
6
21
10.83
143
73.71
7
Reliability
21
10.83
129
66.49
8
39
20.10
116
59.80
9
0
0
117
60.30
Total(5 - 9)
17
8.76
139
71.64
10
25
12.88
130
67.02
11
34
17.54
100
51.54
Responsivenes 12
27
13.93
100
51.54
13
s
7
3.8
147
75.77
Total(10-13)
30
15.47
108
55.67
14
23
11.87
102
52.57
15
41
21.14
85
43.81
16
Assurance
35
18.05
136
70.10
17
11
5.67
140
72.16
Total(14-17)
24
12.38
104
53.6
18
26
13.42
93
47.93
19
25
12.88
94
48.47
20
Empathy
15
7.74
111
57.21
21
14
7.23
95
48.96
22
4
2.07
133
68.55
Total(18-22)
CSQ
Dimensions
377
NFSQ > 0
N
31
60
69
37
32
22
66
30
44
39
77
38
39
60
67
40
56
69
68
23
43
66
75
75
68
85
57
%
15.98
30.92
35.56
19.07
16.5
11.35
34.02
15.46
22.68
20.10
39.7
19.67
20.10
30.92
34.53
20.61
28.86
35.56
35.05
11.85
22.16
34.02
38.65
38.65
35.05
43.81
29.38
Proceedings of Applied International Business Conference 2008
5. Conclusions and recommendations
Increased service quality is believed to have a positive effect on customer loyalty and Profitability in
the contemporary service industry. Accurate measurement of service quality is a major concern to
management. Whereas current service quality evaluation methods for evaluating services are generally
decisive at time those criteria of measurement are fuzzy¸ ambiguous and verbal¸ appling fuzzy method
according to other methods are closet to human thinking. Therefore¸ purpose of this paper is evaluating
five dimensions of service quality in SERVQUAL model by using fuzzy logic that we call Fuzzy
SERVQUAL.
This research follows 3 major objects:
1. Comparing between expectation of clients and performance of customs in 5 dimensions of service
quality.
2. Ranking each of 5 dimensions of service quality in two levels, expectation and performance.
3. Measuring rate of custom service quality (CSQ) and obtaining rate of dissatisfaction, satisfaction
and delight of clients.
For this purpose between customs of around Caspian Sea, three customs in Mzandaran province
(Nowshahr, Sari, and Amir Abad) chose and a questionnaire framed based on SERVQUAL. For
determining reliability of questionnaire, we used Cronbach’s Alpha in our research, that total amount of
is 90.6, therefore the questionnaire has high reliability. Between 210 distributed questionnaires, 194 of
them equal with 92.28 percent are adequate for analyzing. Between people who have answered, there
were 96.39 percent men and only 3.69 percent women and near 50 percent of people who answered
were under 30years old that can indicates to young’s activity in this job.
About first object: by using Wilcoxon signed-rank test according to expectation of clients and
performance of customs, significant differences of each of 5dimensions evaluated. Results shown in
table (8) indicate that in all 5dimensions there are significant differences.
For ranking each of 5dimensions (second object) Friedman’s test has been used that its results has been
shown in table (9). Ranking each of dimensions both in level of expectation and level of performance
have been equal so as:
Reliability – Assurance – Responsiveness – Tangibility – Empathy.
Measuring rate of service quality (object 3) in table (10) according separation of each of dimensions
and entirely has been shown. 71.13 percent of clients implayed their dissatisfaction of CSQ because
custom performance had been lowers than clients expectations. 3.62percent were satisfied and 25.25
percent of them delight of their performance. In other words, we can say 71.13 percent of clients were
dissatisfaction and 28.87percent felt satisfaction of CSQ.
Totally we can say that custom authorities exactly realize clients expectations, but custom performance
was lower than clients expectations in all of the 5dimensions, so these are suggestions to custom
authorities:
1. According to this fact that Mazandaran custom clients appointed highest priority to the “reliability”
dimension and on the other hand 60% of clients are dissatisfaction with custom performance in this
dimension. Our suggestion to Mazandaran custom authority is consist of:
1-1- offer service to clients without mistake and in a proper way at the first time.
1-2- when promising to do an offer on the giving time to clients, perform it exactly.
1-3- emphasize on the presentation of reports, documents and evidence without mistake and error.
1-4- when clients encounter a problem, try to eliminate it.
It is necessary to mention that the upper alternatives have higher priority than the lower ones.
2. The second priority that Mazandaran custom authorities should put on its agenda is “responsiveness”
and we suggest that:
2-1- Tell your clients the exact time of services.
2-2- Offer services to clients without delay.
2-3- Always be ready for answering to the questions of clients.
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3. More than 50% of clients were satisfied with polite and respectful behavior of Mazandaran custom
staff, while these clients were dissatisfaction with the knowledge of staff about answering to the to the
questions (about 70%), therefore Mazandaran custom authorities should try more for training staff.
4. Paying attention to this fact that highest percent of clients dissatisfaction is about “offering services
without mistake and in an appropriate way at the first time” on one hand Mazandaran custom
authorities should give necessary training to their staff and on the other hand, the staff themselves
should have necessary accuracy in offering services.
5. It is necessary to mention that custom authorities should not ignore factors which cause clients
satisfaction and at the first they should try in its maintaining and then in increasing of satisfaction.
These factors are as the following:
5-1- staff politeness with clients.
5-2- staff showing inclination for helping clients in different times.
5-3- smart appearance of staff.
6. Whereas service quality is obtained from a comparison between clients expectations and custom
performance and whenever performance is higher than expectation, idiomatically it causes clients
delight which is a strength point for the custom. Factors which led to the delight of Mazandaran custom
clients and custom authorities shouldn’t ignore are shown in the following:
6-1- appropriate custom work time for clients.
6-2- recognition of special needs of clients.
6-3-saftey feeling in intercourse with the custom.
6-4-smart appearance of the staff.
6-5-staff inclination for elimination of clients problem.
7. Whereas staff are known as one of the main integrated components of service marketing, in
Mazandaran province customs in most cases relevant to the staff a relative satisfaction exist with them
and not with their knowledge it answering the clients questions, so it is necessary that Mazandaran
customs authorities eliminate this deficiency by holding training courses.
References
Abbott, J.E. (1996) Measuring quality with fuzzy logic. The Tourist Magazim , 8, 4, 36-39.
Amirshahi , S.A and Mazhari, SH.(2008) Consumer ethnocentrism in small appliances industry of Iran.
Journal of Marketing Management, l2,3,71-88.
Arasil , H., Ekiz, E.H., Katircioglu, S.T. ( 2008) Geering service quality into public and private
hospitals in small islands. International Journal of Health Care Quality Assurance, 21, 1, 8-23.
Arasli, H., Mehtap-Smadi, S. and Katircioglu, S.T. (2005) Customer service quality in the Greek
Cypriot banking industry. Managing Service Quality, 15, 1, 41-56.
Baggs, S.C. and Kleiner, B.H.(1996) How to measure customer service effectively. Managing
Service Quality , 6, 1, 36–39.
Dubois, D. and Prade, H. (1978) Operations on fuzzy number. International Journal of Systems Science,
9, 613–626.
Friedlob,G.T. and Schleifer ,L.L.F.(1999) Fuzzy logic: Application for audit risk and uncertainty.
Managerial Auditing Journal, 14/3, 127-135.
Ghobadian, A., Speller, S. and Jones, M. (1994) Service quality concepts and models. International
Journal of Quality & Reliability Management, 11, 9, 43-66.
Islamic Republic of Iran’s Customs Administration. [Online available at www.irica.gov.ir accessed on
October 15, 2008].
Jannadi , O.A., and Al-Saggaf,H. (2000) Measurement of quality in Saudi Arabian service industry.
International Journal of Quality & Reliability Management, 17, 9, 949-965.
Kahraman,C., Ates, N.Y., Cevik, S., Gulbay, M. and Erdogan, S.A. (2007) Hierarchical fuzzy TOPSIS
model for selection among logistics information technologies. Journal of Enterprise
Information Management , 20, 2, 143-168
Kerin , R.A., Hartley, S.W. and Berkowitz, E.N. (2006) Marketing. 8th ed. Graw-hill.
Liou ,T.S. and Chen,C.W. (2006) Subjective appraisal of service quality using fuzzy linguistic
assessment. International Journal of Quality & Reliability Management, 23, 8, 928-943.
Magi, A. and Julander, C.R. (1996) Perceived service quality and customer satisfaction in a store
performance framework. Journal of Retailing and Consumer Services, 3,1, 33-41.
379
Proceedings of Applied International Business Conference 2008
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985) A conceptual model of service quality and its
implications for future research. Journal of Marketing, 49, 4-50.
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988) SERVQUAL: A multiple-item scale for
measuring consumer perception of service quality. Journal of Retailing, 64, 1, 12-40.
Tsuar, T.Y. Chang and C.H. Yen. (2002) Evaluation of airline service quality by fuzzy MCDM,
Tourism Management, 23, 107–115.
Zadeh, L. A. (1965) Fuzzy sets. Information and Control, 8, 338–353.
Zhao, X., Bai, C., and Hui, Y.V. (2002) An empirical assessment and application of SERVQUAL in a
Mainland Chinese department store. Total Quality Management, 13,2, 241-54.
380