Certification and Inspection Service Quality Applying the fuzzy SERVQUAL method

■2012 JSPS Asian CORE Program, Nagoya University and VNU University of Economics and Business

Certification & Inspection Service Quality:
Applying the fuzzy SERVQUAL method

CUI Li-xin 1, ZENG Guang-feng 2, WU Hong-yan2, WANG Cheng-jie 2, LIU Ru 1

ABSTRACT: This paper applied fuzzy set theory based on modified SERVQUAL model to analysis service quality in
certification & inspection industry in China. The study consists of 405 randomly selected participants who are customers
of China Certification & Inspection Group (CCIC). The paper includes four parts: introduction, methodology, a case
study of certification & inspection service quality and conclusions. The results of this research show that among the five
dimensions the feature of “tangible” has the biggest gap between the service quality expectations and perceptions. So, the
company we studied (CCIC) need to increase investment in tangible aspects in order to improve their service quality
efficiently.
KEYWORDS: SERVQUAL

certification & inspection

1. Introduction

fuzzy set theory

Blesic et al., 2011), higher education(Ishfaq Ahmed et

Service quality is more difficult to be evaluated

al.,

2010;

Kashif

Hussain,

2011),

urban

than commodity quality but it plays an especially

transportation(Seyed Mohammad Mahmoudi et al.,

important role in firms to improve customer

2010; Anjali Awasthi et al., 2011), public or private

satisfaction and customer loyalty. For measuring the

health care(Raymond Tempier et al., 2010; Tashonna R.

service quality, a 22-item questionnaire instrument

Webster

called SERVQUAL was proposed by Parasuraman et

system(Narasimhaiah Gorla, 2011), e-learning(Godwin

al. in 1988 (A. Parasuraman, V. A. Zeithaml, L. L.

J. Udo et al.,2011), hot spring industry(Shun-Hsing

Berry,

Chen

1988).

Since

then,

the

SERVQUAL

et

et

al.,

al.,

2011),

2011),

information

condominium

questionnaire has been used to analyze service quality

management(Yao-Chen Kuo et al., 2011), internet

within diverse organizations, such as retailing

service(Godwin J. Udo et al., 2010;Gregory John Lee,

organizations(Halil
industry(James

J.

Nadiri,
H.

Liou

2011),

airline

2011),

et

2011),

banking(Mina Beigi, Melika Shirmohammadi, 2011),

al.,

supply

chain(Gyan

Prakash,

2011),

restaurants(Jang-Hyeon Nam et al.,2011), hotel(Prabha

and so on. But the measurement of

Ramseook-Munhurrun, Perunjodi Naidoo, 2010; Ivana

hasn't been applied to certification & inspection

1
2

School of Management and Economics, Beijing Institute of Technology, Beijing, China
China Certification & Inspection Company, Beijing, China

SERVQUAL


Suppose A

a, b, c is a triangular fuzzy number

as Fig. 1. Then, suppose the membership function of

A

A is f A x .

LA

RA

fA x

a

x a
b a

a

x b

c x
c b

b

x c

c

b

0
Fig. 1. Triangular fuzzy number A

LA x

x a
，a
b a

x

b,

RA x

c x
，b
c b

x

c.

LA1 h

a

b a h，
0 h 1,

RA1 h

a

c a h，
0 h 1.

industry so far. Qin Su et al. (2010) just mentioned that
SERVQUAL maybe not suitable for certification
industry and in their research they applied Indserv
model which is a measurement designed specifically

else

for BtoB industry.
In previous researches, methods being used are
various, including fuzzy set theory (Chien-Chang Chou,
al., 2009), fuzzy AHP (Chen Guiyun et al., 2006),

L A x and R A x are the left function and
right function of the triangular fuzzy number A ,

grey-fuzzy DEMATEL approach (Tseng Ming-Lang,

respectively. While L A1 h

2008), Structural Equation Modeling (Lin Deng-Juin et

and R A1 h

are the

inverse functions of the function L A x

and the

2009), modified grey relation method (James J. H.
Liou et al., 2011) and so on.
This paper applied fuzzy set theory based on

function R A x , respectively.

modified SERVQUAL model to analysis service
quality in certification & inspection industry in China.

method for one fuzzy number is proposed by Chen and

2. Methodology
The fuzzy set theory used in this paper was
introduced by Zadeh (1965) firstly. In fact, fuzzy set
theory

has

been

The graded mean integration representation

applied

in

solving

many

decision-making problems. In this paper, a combined

Hsieh(1998). This method is based on the integral
value of graded mean h-level of fuzzy number. In
detail, suppose the graded mean h-level value of fuzzy
number A is h LA1 h

RA1 h / 2 (the graded mean

fuzzy SERVQUAL method will be used to copy with
the measurement of service quality in certification &
inspection industry. In this section, the basic definitions
of fuzzy set theory are briefly presented as follows:
2.1 The concept of fuzzy number
Because of the simplicity of the concept and
computation for triangular fuzzy number set, it is
widely used in practical applications (Pedrycz, 1994).

h-level value of fuzzy number A as Fig. 2.).

During the research of the measurement of service
quality in the certification & inspection industry, there

A

are 3 steps involved. They are questionnaire designing,

h

interview survey, calculation for collecting data.

LA

RA

3.1. Questionnaire design
In this research, the questionnaire designing is
based on the previous literatures and the interview of
the interviewees came from China Certification &

LA1 h

a

Fig. 2.

c R A1 h

b

Inspection Group (CCIC). In the questionnaire, there are

5 major dimensions and 22 items.

The graded mean h-level value of

3.2. Interview survey

LA1 h

fuzzy number A

All the interviewees are the customs of CCIC who

The graded mean integration representation of A

have already accepted the service provided by CCIC.
is P A .

Participants are randomly selected. The survey spent
1

P A

h LA1 h
2

0
1

R A1 h

h a

about 3 weeks. The size of the sample is 405. The
1

dh

b a h a

0

c a h

2

0

response rate is nearly 100%.

hdh

3.3. Calculation for collecting data

dh

Based on fuzzy set theory, the basic arithmetic
operations of fuzzy numbers have been clearly

triangular

fuzzy

a2，b2，c2

A2

is

and

a
also

is another triangular fuzzy

a2，b1 b2，c1

A2

a1 c2，b1 b2，c1

c2

（2）

service to customers. There exist gaps between the

responsiveness、reliability、empathy、assurance. The
Company we studied (CCIC) should give the most
priority of increasing investment in the visual image of

a2

（3）

3. A case study of certification & inspection service
quality

conclusion that CCIC doesn’t provide satisfactory

dimensions from high to low as following: tangible、

(2)Subtraction of fuzzy numbers

A1

service quality can measure a company’s service

dimensions. We sort these gaps among different

(1)Addition of fuzzy numbers

a1

between the expectations and the perceptions of the

expectations and the perceptions of all service quality

fuzzy numbers as follow.

A2

The fuzzy SERVQUAL method is a combination

quality level. From the study, we can come to the

number. We present the basic arithmetic operations of

A1

4. Conclusions and suggestions
of SERVQUAL model and fuzzy set theory. The gaps

a1，b1，c1

number

gap between expectation and perception are shown in
Table 1 and Table 2.

2.2. The arithmetic operations on fuzzy numbers

Suppose

hdh The scores of expectations and perceptions for
of the scores of expectations and perceptions, and the

（1）

described.

0

service quality are calculated, respectively. The result

1
a 4b c
6

A1

1

the company in order to improve customers’ perception
of its services.


Table 1 Scores of fuzzy perceptions and expectations
Dimensions

Fuzzy perception

Fuzzy expectation

Fuzzy gap

Responsiveness

(6.139,8.134,8.883)

(6.422,8.422,8.963)

(-2.824,-0.288,2.46)

1

(6.107,8.102,8.840)

(6.421,8.421,8.956)

(-2.849,-0.319,2.419)

2

(6.197,8.192,8.905)

(6.421,8.421,8.972)

(-2.775,-0.229,2.484)

3

(6.152,8.147,8.925)

(6.436,8.436,8.978)

(-2.826,-0.289,2.489)

4

(6.156,8.151,8.899)

(6.415,8.415,8.950)

(-2.794,-0.264,2.484)

5

(6.084,8.078,8.843)

(6.417,8.417,8.961)

(-2.878,-0.338,2.426)

Assurance

(6.239,8.246,8.983)

(6.470,8.470,8.985)

(-2.746,-0.223,2.513)

6

(6.217,8.212,8.935)

(6.5,8.5,8.989)

(-2.772,-0.289,2.435)

7

(6.289,8.284,8.960)

(6.482,8.482,8.978)

(-2.689,-0.198,2.478)

8

(6.344,8.39,9.16)

(6.490,8.490,8.989)

(-2.645,-0.1,2.67)

9

(6.105,8.1,8.875)

(6.407,8.407,8.983)

(-2.878,-0.307,2.468)

Reliability

(6.224,8.219,8.924)

(6.489,8.489,8.982)

(-2.758,-0.271,2.435)

10

(6.09,8.085,8.895)

(6.459,8.459,8.967)

(-2.877,-0.374,2.436)

11

(6.199,8.194,8.925)

(6.504,8.504,8.989)

(-2.79,-0.31,2.421)

12

(6.264,8.259,8.92)

(6.492,8.492,8.989)

(-2.725,-0.233,2.429)

13

(6.342,8.337,8.955)

(6.503,8.503,8.983)

(-2.642,-0.166,2.452)

Tangibles

(5.782,7.775,8.8)

(6.308,8.308,8.949)

(-3.166,-0.533,2.491)

14

(5.809,7.799,8.769)

(6.3,8.3,8.939)

(-3.13,-0.501,2.469)

15

(5.875,7.87,8.832)

(6.335,8.335,8.961)

(-3.086,-0.465,2.497)

16

(5.645,7.639,8.77)

(6.294,8.294,8.950)

(-3.305,-0.655,2.476)

17

(5.777,7.77,8.806)

(6.345,8.345,8.955)

(-3.178,-0.572,2.461)

18

(5.806,7.796,8.821)

(6.268,8.268,8.939)

(-3.132,-0.472,2.553)

Empathy

(6.251,8.246,8.913)

(6.494,8.494,8.989)

(-2.738,-0.249,2.419)

19

(6.368,8.363,8.95)

(6.506,8.506,8.989)

(-2.621,-0.142,2.445)

20

(6.147,8.142,8.885)

(6.481,8.481,8.989)

(-2.842,-0.3392.405)

21

(6.284,8.279,8.92)

(6.515,8.515,8.994)

(-2.711,-0.237,2.405)

22

(6.206,8.201,8.894)

(6.475,8.4745,8.983)

(-2.777,-0.274,2.42)


Table 2 Scores of perceptions and expectations
Dimensions

Perception

Expectation

Gap

Responsiveness

7.926[4]

8.179[4]

-0.253[2]

1

7.893[15]

8.177[14]

-0.284[9]

2

7.978[10]

8.180[13]

-0.202[18]

3

7.944[11]

8.193[12]

-0.249[11]

4

7.943[12]

8.171[16]

-0.228[15]

5

7.873[17]

8.174[15]

-0.301[7]

Assurance

8.034[1]

8.222[3]

-0.188[5]

6

8.000[7]

8.248[5]

-0.248[13]

7

8.064[4]

8.231[9]

-0.167[19]

8

8.177[1]

8.240[7]

-0.063[22]

9

7.897[14]

8.170[17]

-0.273[10]

Reliability

8.004[3]

8.238[2]

-0.234[3]

10

7.888[16]

8.210[11]

-0.322[6]

11

7.983[9]

8.252[3]

-0.268[11]

12

8.036[6]

8.241[6]

-0.205[17]

13

8.107[3]

8.250[4]

-0.142[20]

Tangibles

7.614[5]

8.082[5]

-0.468[1]

14

7.629[20]

8.073[20]

-0.444[3]

15

7.698[18]

8.106[19]

-0.408[5]

16

7.495[22]

8.070[21]

-0.575[1]

17

7.612[21]

8.113[18]

-0.501[2]

18

7.635[19]

8.047[22]

-0.411[4]

Empathy

8.025[2]

8.243[1]

-0.218[4]

19

8.129[2]

8.253[2]

-0.124[21]

20

7.933[13]

8.232[8]

-0.299[8]

21

8.053[5]

8.262[1]

-0.209[7]

22

7.984[8]

8.226[10]

-0.242[14]

ACKNOWLEDGMENTS
This research was supported by the Grant-in-Aid for
Asian CORE Program "Manufacturing and
Environmental Management in East Asia" of Japan
Society for the Promotion of Science (JSPS).
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