Journal of Service Science and Management, 2011, 4, 42-51
doi:10.4236/jssm.2011.41007 Published Online March 2011 (http://www.SciRP.org/journal/jssm)
Analysis of Logistics Service Attributes Based on
Quantitative Kano Model: A Case Study of
Express Delivering Industries in China
Qingliang Meng, Nongji Zhou, Jian Tian, Yijia Chen, Fen Zhou
School of Management & Economics, Jiangsu University of Science and Technology, Zhenjiang, China.
Received November 26th, 2010; revised December 27th, 2010; accepted December 29th, 2010.
ABSTRACT
Considering the non-linear relationship between product attributes and customer satisfaction, Kano’s model is widely
used in the area of qua lity man agem ent and produ ct inno vation . In order to add ress th e deficiencies o f trad itiona l Kano
method in qualitative analysis and subjective classifica tion criteria, a quantitative Kano model is set up. By the building
of Kano Quantitative satisfaction index and importance index, an objective classification method and the decision-
making rule to improve service q uality are pro posed . Then a well-estab lished logistics service attrib utes analysis model
based on quantitative Kano model has come up. The model is illustrated through a ca se study of express delivering in-
dustries in China.
Keywords: Logistics Service Attributes, Kano Model, Qualitative Analysis, Express Deliverin g Industries
1. Introduction
Based on Herzberg’s ‘Motivator-Hygiene Theory’, Kano
et al. (1984) defined the product quality element of dif-
ferent categories that impact customer satisfaction in
different ways. Which namely: attractive quality attribute,
must-be quality attribute, one-dimensional quality attrib-
ute, indifferent quality attribute and reverse quality at-
tribute [1]. Using Kano’s model, quality attributes that
have the greatest influence on customer satisfaction can
therefore be identified, and these can then be used to fo-
cus on priorities for product or service development and
improvement [2]. With such advantage, Kano model is
widely used in quality management [3], new product
development [4-6] as well as QFD integration [7-9].
Using Kano model to analyze product or service at-
tribute, we can consider both customer psychology ele-
ments and customers’ consume motivation, witch can
make up the flaw of the data mining tool, because apply-
ing a data mining technique in capturing customer know-
ledge is done by collecting and tracking lots of transact-
tional data only to obtain purchasing behavior knowledge
about customers.
The Kano diagram provides a rough sketch of the cus-
tomer’s satisfaction in relation to the product and service
performance level. In such a sense, it only allows quail-
tative assessment of product and service attributes [10],
and the resulting Kano category is still qualitative in na-
ture, which could not precisely reflect the extent to which
the customers are satisfied [11]. these limitations make it
fall short to play a key decision-making role in product
innovation and service management. Therefore, Berger
et al. (1993) proposed a graphical Kano diagram that is
based on predefined scales related to the customer’s sat-
isfaction and dissatisfaction. Each customer requirement
can be represented as a pair of satisfaction and dissatis-
faction values [11]. Yang,Ching-Chow (2005) redefined
the Kano model by integrating the analysis of the impor-
tance-satisfaction(I-S model), and the classified result of
Kano model was redefined from four kinds to eight kinds
[3]. Considering the customer sensation fuzziness to-
wards the product or service attribute, Yu-Cheng Lee,
Sheng-Yen Huang (2009) proposed a kind of fuzzy Kano
model design concept [12].
2. Kano Model
According to different types of relationship between qua-
lity attributes and customer satisfaction, Product quality
elements are classified into attractive quality elements,
one-dimensional quality elements, must-be quality ele-

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of
Express Delivering Industries in China
43
ments, indifferent quality elements, and reverse quality
elements using Kano’s model [1]. Kano model is illus-
trated in Figure 1.
 Attractive quality element: its full functionality will
definitely incur customers’ satisfaction; however,
dissatisfaction will not be sensed if absent.
 One-dimensional quality element: customer satisfac-
tion is proportional to the degree of its function ful-
fillment. It is a normal requirement of customer de-
mands, where the higher the product functionality
quality, the higher customer satisfaction will be.
 Must-be quality element: customer takes its full func-
tionality for granted; failing to fulfill the function
certainly provokes strong customer dissatisfaction;
however, its presence increases no satisfaction.
 Indifferent quality element: Customer does not matter
whether it is functional or not.
 Reverse quality element: Customer is dissatisfied
when it is functional, satisfied when it is not func-
tional.
For better customer satisfaction and less customer dis-
satisfaction, a company has to make every effort to offer
attractive quality elements in new products as well as
eliminate possible defects on must-be quality elements.
Because the judgment of attractive quality or must-be
quality is highly personal, it is tacit knowledge embedded
in customers’ mind. Scholars also pointed out that must-
be quality elements are normally less implicit, while at-
tractive quality elements are hidden, implicit, so it is de-
sirable for a company to take the hypothesis-testing ap-
proach to survey the market. Therefore, for a company,
the most important task is how to survey the customers in
the market to excavate all the implicit customers’ know-
ledge and convert them into explicit customer knowledge
Figure 1. Kano model of quality attributes.
that can be transformed into a successful product.
3. Design of Quantitative Kano Model
Matzler et al. (1998) pointed out that the convenient way
to quantify Kano model is to evaluate the customers’
satisfied or dissatisfied level towards products or service
performance [2]. That means to design customers’ satis-
faction scale of positive or negative problems towards
products or service attributes as showed in Table 1. Be-
cause the positive answer is stronger than the negative
answer, the asymmetry scale is designed to reduce the
impact of negative evaluation [13].
In addition, the traditional Kano model doesn’t con-
sider the customers’ importance perception towards each
product or service attribute. Combined with Yang’s re-
search results [3], we can integrate customers’ impor-
tance perception in the questionnaire. The specific scale
is showed in Table 2.
If a product or service attribute expressed as


1, 2,,
i
F
fi I,
F
is the product or service at-
tribute set, i
f
is the ith product or service attribute,
J
is the total amount of the interviewed customers, ac-
cording to the re-design Kano questionnaire, we can get
customers’ evaluation towards each product or service
attribute


1,
i2, ,
f
iI :


,,
ijij ijij
exyw (1)
Among them, ij
x
is the th customer’s evaluation
towards the negative problem of product or service at-
tribute i
j
f
; ij is the th customer’s evaluation to-
wards the positive problem of product or service attribute
i
y j
f
; ij is the th customer’s importance evaluation
towards the product or service attribute
w j
i
f
.
For each product or service attribute i
f
, customers’
average satisfaction level towards the negative problem
is defined as i
X
; customers’ average satisfaction level
towards the positive problem is defined as i
Y, so there
are:
11
11
,
J
iijijiij ij
jj
YwyX w
JJ



J
x (2)
The value of


ii
,
X
Y can trace in the two-dimen-
sional coordinates chart, the horizontal dimension is the
customers’ dissatisfaction degree towards product or
service attribute i
f
and the vertical dimension is the
satisfaction degree. Most

ii
,
X
Y
i
should be in the
range of 0-1, the negative value is the reverse quality
factors or the questionable answer which shouldn’t be
included in the calculation of the average value. So the
product or service attributes
f
can be described as a
vector, namely


,
iii
r

r, where, 22
iii
X
Yr,

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of
Express Delivering Industries in China
44
Table 1. Satisfaction scale of positive or negative problems.
I like it very muchIt must be this wayI am neutral I can live with it I don’t like it
With the attribute 1 0.5 0 −0.25 -0.5
Product or
service attribute Without the attribute −0.5 −0.25 0 0.5 1
Table 2. Importance scale.
Unimportant Somewhat important Important Very important Extremely important
0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0

1
tan i
ii
YX 

.
We called the distance of vector as Kano impor-

i
r

tance index, 0
i
r2. Angle is called as Kano
i

satisfaction index, 0
i2

  . If we use Kano satis-
faction index and Kano importance index as two dimen-
sions, the domain of product or service attributes can be
divided into four quadrants. According to different level
of satisfaction degree and importance degree, we can
propose the improvement decision-making rule for a
product or service attribute specifically. And this will be
illustrated through a case study of express delivering
industries in China.
4. Detection Process of Logistics Service
Based on the Quantitative Kano Model
Along with the economy developed rapidly, logistics
enterprises in China face many challenges which cus-
tomers require to improve logistics service quality fast.
Therefore, it is necessary for these enterprises to make
the proper logistics service strategic plan based on the
service design orientation. Some scholars studied the
problem about the design and plan of logistics service
capacity with QFD [14-15] and other scholars studied the
optimized problem of logistics service with fault tree
[16]. These researches played an important role in im-
proving logistics services. Considering that with Kano
model to detect product or service attribute, we can inte-
grate customer psychological factors and customers’
consume motives, so we will analyze the logistics service
attributes for quality improvement based on quantitative
Kano model.
The purpose to analyze logistics service attributes is to
identify the key component elements of logistics service
from customer perspective as well as the priority of these
attributes in the decision-making for quality improve-
ment. Based on the customer survey data, combined with
the classified criterion of customer satisfaction factors of
quantitative Kano model, the concrete classified results
of logistics service attributes cab be gained. And if we
use Kano satisfaction index and Kano importance index
as two dimensions, we can find out the priorities of these
attributes. The detection process of logistics service
based on the quantitative Kano model is showed in Fig-
ure 2.
4.1. Attributes Distinction of Logistics Service
Woodruff proposed that customer value of products or
service is the customers’ sensation preference and ap-
praisal towards the product or the service attribute and
the attribute potency as well as achieving the purpose by
the using result. In other words, the customer value is a
spatial structure which includes three levels: the service
attribute, the service effect and the using result. The
connection of these three levels constitutes a way —
means-end chain. From bottom to up, attribute is the
method to achieve the effect, the effect is the method to
achieve customer goal, vice versa.
In the bottom of the model, customers regard the ser-
vice as the coalition of specific attribute. They will form
a kind of expectation and preference which is the value
they want to own reflecting in customer value according
to the contribution of specific service attribute to realiz-
ing the expectation result when they purchase and enjoy
the service. Meanwhile, customers can achieve their own
goal according to the service attributes and form the ex-
pectation of the specific using results. Satisfaction degree
can be produced in each level of customer’s expectation
value level model, and the total satisfaction degree is
decided by the sum of customer satisfaction degree in
different levels.
Therefore, the enterprise should distinguish the bene-
fits that the logistics service brings to customers accord-
ing to the customers’ perceive value towards the attrib-
utes, characteristics and the functions of a latent logistics
service when any item logistics service concept is put
forward. The logistics enterprise can adopt the analysis
method of means-end chain to analyze each logistics
service to distinguish the key elements that compose the

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of
Express Delivering Industries in China
45
Figure 2. Analysis process of logistics service attributes based on quantitative Kano model.
logistics service through the service attributes, the cus-
tomer using result as well as customer’s ultimate object-
tive by the bidirectional communication with customers
and form many attributes of logistics service, then the
Kano questionnaire of logistics service attribute can be
designed. The customers can make the response accord-
ing to the benefits that are brought by these logistics ser-
vice attribute. When these customers’ attitudes towards
service are collected by data form, the enterprise gains
the customer knowledge about the customer preference
to logistics service.
4.2. Classification of Customer Satisfaction
Quality Elements
Through integrating the Kano questionnaire for “human
data” with a conventional interval rating scale survey
instrument for ‘transaction data’ to collecting primary
data, customers’ preferences to a logistics service attrib-
ute can be understood. In every questionnaire, an indi-
vidual customer presents his/her own specific preference
pattern, his/her demographic data, and the monetary
value he/she is willing to pay for certain logistics service
features. The aggregate of questionnaires by all sample
customers constitutes the database for Kano’s Method to
realize customer satisfaction clues’ categorization in
terms of attractive, must-be, one-dimensional, indifferent,
and reverse quality elements. At this stage, the company
acquires knowledge ‘about’ customers by understanding
customers’ background, expectation, and preference on
logistics service attributes.
4.3. Customer Segmentation
Customer segmentation refers to the process that the en-
terprise divides a whole service market into some certain
customer groups, according to differences of customers’
demands and desires, purchase behaviors or customer
value. According to Kotler’s marketing theory, the en-
terprise is changed from be organized based on the
product units to be organized based on customer seg-
ments. He proposed the market can be divided according
to two variables [17]: 1) customer characteristics, such as
geography characteristics, population statistic character-
istics and psychological variables and so on; 2) behavior
variables, such as customer attitudes about service, cus-
tomer responses about benefits so on. Using the data
gained from the sub-process of “customer satisfaction
classification” to do the correct market segmentation
based on the related customer characteristics. After the
segmentation, the concrete characteristics of each cus-
tomer group can be distinguished and service attributes
classification from different customer groups can be
found.
4.4. Extraction of Customer Usage Pattern
Once the customer segmentation task is done, the attract-
tive quality element, must-be quality element, etc, for that
segment of customers, are also determined by Kano’s
Method to delineate the prospective customer usage pat-
terns in each segment. Therefore, the knowledge from
customers supports the logistics company to serve the
right market segments and make appropriate strategic
business decisions in the logistics service development
plan and marketing activities. By a prospective customer
usage pattern extracted from the aggregate customer
sample, a company may revise the original logistics ser-
vice’s definition and set priorities for service attributes to
be developed, enhance the functionality of the attractive
quality element, ensure no defect on the must-be quality
element, improve the performance of the must-be quality
element, and rule out service attributes categorized as
reverse and indifferent quality elements. Market seg-
mentation also provides further insight for the perspec-
tive usage pattern in each segment. The differentiated
pattern demonstrated by different segments also becomes
useful information for a logistics company to take the
right tactics for improve service quality to satisfy various
segments.
5. Case Study
With the development of economics in China, many en-
terprises and people are desired high logistics service
quality of express delivery industries. But few people are
satisfied to service quality of the express delivery Indus-
tries. So it is critical to acquire customer knowledge to

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of 46
Express Delivering Industries in China
aid in express delivery service planning and conducted a
pilot market survey introducing the quantitative Kano
model. In this project a hybrid questionnaire combining
Kano’s method with a conventional interval ratings scale
instrument was created to extract customer knowledge.
Based on the survey of many Chinese express delivering
industries and we get some representative express service
attributes as shown in Table 3. And Table 3 also de-
scribes the relationship between the each of the benefits
that provide for customers.
5.1. Design of Kano Questionnaires
The first part of the questionnaire is related to demo-
graphic characteristics of customer-related information.
The second part of the questionnaire is the survey of ex-
press service-related attributes, a total of 26 pairs of en-
tries, and each entry is designed according to the form
shown in Table 1 . The upper row is the positive problem
part of Kano questionnaire, while the lower row is the
negative problem part of Kano questionnaire. The ques-
Table 3. Express service attributes and the benefits it provides.
Service attributes Description of logistics service attributes Benefit provided for customers
f1 Arrive today by air (before 22:00 of today) fast
f2 Arrive today by land (before 22:00 of today) fast
f3 Arrive next day fast
f4 Keep 7 days free for the goods that isn’t delivered safe, convenient
f5 Service for 365 days convenient
f6 Realize man-made, self-orders, express item inquiry and other functions
through call center convenient, fast
f7 Collection payment for arrived cargo (free) safe, convenient
f8 Value insured service (5‰ of the statement value ) Convenient, value-added
f9 Delivery after noticed convenient, timely
f10 Night pick-up service convenient
f11 Write commission safe
f12 MSG short message notification convenient, safe
f13 Packing service value-added, safe
f14 Self-check convenient
f15 Delivery staff with friendly attitude and professional cheerful, convenient
f16 Delivery staff is familiar with the company philosophy cheerful, safe
f17 Delivery staff dress clean and tidy uniform cheerful
f18 Brand trust, a sense of security safe
f19 Expand the related business positively and rapidly convenient, fast
f20 Free delivery documents beneficial
f21 Complete appointment orders and pick within 1 hour fast
f22 Complete in 2 hours from arriving the shop to deliver fast, safe
f23 Pick up time appointment with telephone convenient
f24 Customers can pick up the goods himself in an uncover network region Increase customer cost
f25 Communicate easily with a delivery staff cheerful, convenient
f26 Communicate easily with a call center staff cheerful, convenient

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of
Express Delivering Industries in China
47
tionnaire integrates the Kano questionnaire survey me-
thods and traditional 5 scale survey methods.
Table 4 is quality factors classification of Kano model
in which “A” represents attractive quality attribute, “O”
represents one-dimensional quality attribute, “R” repre-
sents reverse quality attribute, “M” represents must-be
quality attribute, “I” represents indifferent quality attrib-
ute. “Q” represents questionable answer.
5.2. Data Collection
The respondents were selected randomly to do a survey
from an express company’s customers, and the survey
was conducted in two forms: E-mail and face to face
survey. 130 questionnaires were distributed; the period is
from May 1, 2010 to May 31, 2010. And finally, 87
questionnaires were recovered, 83 questionnaires were
available, the effective questionnaires response rate was
63.8%, the specific sample characteristics are in Table 5.
5.3. Date Analysis
Based on the quantitative Kano model, statistical results
are shown in Table 5. In order to detect the category of
the express quality service attribute, supposing the value
of importance indicators is 0.5, for service attribution i
f
,
if iand , i 0.5x0.5
i
y
f
is considered unimportant
and divided into indifferent quality; and if i and
i, i
0.5x
0.5
y
f
is considered as must-be quality. Similarly,
if i and , 0.5x0.5
i
yi
f
is thought to be one-di-
mensional quality; if i and i, i x0.5 0.5y
f
is
called as the attractive quality. This can be shown in
Figure 3. Table 6 is the final classification results of
express service attributes.
5.4. The Findings and Discussions
For the aggregate customers, Table 6 shows that the two
express delivery service: service for 365 days and MSG
short message notification are regarded as attractive
quality elements, it shows that the aggregate customers
would be delighted by these two service attributes. The
customer aggregate also prefers more functionality on
items such as delivery after noticed, communicating eas-
ily with a delivery staff and other 5 service as these items
are regarded as must-be quality elements. And arriving
today by land, arriving next day, express items inquiring,
other functions realized through call center and other 12
service attributes are regarded as must-be quality ele-
ments. Meanwhile, arriving today by air, collection pay-
ment, delivery staff is familiar with the company phi-
losophy and other 7 service attributes are perceived as
indifferent quality.
Customers in different segments do demonstrate a dif-
ferentiated perspective usage pattern. For example, self-
check and CTI synthesis information service system and
other service attribute are perceived as attractive quality
elements by college students. The segment consists of
many young college students customers who show a
strong propensity to appreciate specific features such as
internet web browsing. And the service attributes such as
arrive today by land, delivery after noticed, delivery
quickly after express mail arrive at outlets and commu-
nicate easily with a call center staff are perceived as
must-be quality elements by both individual workers and
state owned enterprise customers, but these service at-
tributes are perceived as different quality elements by
college students and private enterprise customers. It can
be found that individual workers and state owned enter-
prise customers attach more attention to their profession-
alism and safety when they use the express delivery ser-
vices. One important reason is that these two customer
groups often express some relatively expensive items
(such as the company contracts, invoices, receipts of
goods, etc.).
The knowledge ‘from’ customers in different market
segments discovered through Kano’s Method becomes a
valuable asset for an express delivery company to deploy
further its detailed business strategies to precisely target
those market segments for greater marketing accom-
plishments.
Table 4. Quality attributes categories of Kano model.
With the service attribute
like Must-be neutral Live with dislike
like Q R R R R
Must-be A I I I R
neutral A I I I R
Live with A I I I R
Without the service attribute
dislike O M M M Q

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of 48
Express Delivering Industries in China
Table 5. Perception that expressed by customers for service attributes.
College students (36) individual workers (22)Private Enterprise (13) State Owned
Enterprise (12) Overall (83) Service
attributes
i X i Y i X i Y i X i Y i X i Y i X i Y
f1 0.1625 −0.0065* 0.1052 0.4510 0.3437 0.2260 0.8062 0.4485 0.0990 0.0388
f2 0.1313 −0.0125* 0.3471 0.4472 0.0761 0.2023 0.8826 0.3649 0.9128 0.2646
f3 0.4063 0.5375 0.6019 0.0886 −0.0753* 0.3820 0.8161 0.4793 0.8365 0.4713
f4 0.5500 0.4750 0.5134 0.3478 0.0817 0.4890 0.6443 0.2217 0.7966 0.4050
f5 0.4375 0.2813 0.1297 0.9069 0.4430 0.7846 0.2676 0.5404 0.0936 0.5700
f6 0.4000 0.5500 0.5340 0.7273 0.2510 0.1358 0.7823 −0.1005* 0.7176 0.1958
f7 −0.0188 * 0.0063 0.4700 0.4785 0.3066 −0.0722*0.5862 0.6791 0.4759 0.2297
f8 −0.0125* 0.0500 0.2802 0.0977 0.5415 0.7035 0.9419 0.3877 0.1430 0.1832
f9 −0.0313* 0.0500 0.9238 0.8625 0.0268 0.1872 0.8809 0.6731 0.6221 0.6063
f10 −0.0224* 0.0500 0.2570 0.3615 0.0334 0.2412 0.7529 0.1163 0.0527 0.2199
f11 −0.0115* 0.0457 0.8022 −0.0389*0.6049 0.3714 0.6984 0.0004 0.9130 0.0789
f12 0.4550 0.5765 0.3401 0.9771 0.3403 0.6165 0.1213 0.9115 0.3662 0.6495
f13 0.5565 0.4575 0.5382 0.8813 0.7262 0.6599 0.9447 0.1928 0.6072 0.4535
f14 0.4550 0.5765 0.1252 0.8442 0.2680 0.5110 0.5765 0.3557 0.6863 0.2648
f15 0.5565 0.8576 0.6768 0.8494 0.7890 0.8050 0.8326 0.9725 0.9405 0.6767
f16 0.1030 0.0298 0.1812 0.4851 0.2495 0.3865 0.2137 0.3714 0.4579 0.2660
f17 0.7186 0.3345 0.7976 0.2538 0.6148 0.0690 0.6969 0.3724 0.5118 0.1242
f18 0.8295 0.2266 0.6950 0.0403 0.5002 0.1324 0.9233 0.3685 0.9581 0.0614
f19 0.1467 0.0347 0.5500 0.7116 0.1805 0.1776 0.8426 0.6239 0.8164 0.5788
f20 0.8018 0.4451 0.8524 0.0041 0.5064 0.0999 0.5321 0.2902 0.6589 0.2520
f21 0.6236 0.6709 0.8166 0.6448 0.5890 0.9993 0.6840 0.2326 0.5543 0.5201
f22 0.1420 0.0975 0.9334 0.3484 0.8971 0.9437 0.5320 0.3416 0.5745 0.1690
f23 0.2438 0.0855 0.1191 0.4434 0.3378 0.3274 0.0783 0.0145 0.4713 0.2180
f24 0.4777 0.3472 0.4928 0.3844 0.2053 0.2580 0.2814 0.4180 0.2116 0.1551
f25 0.6748 0.6672 0.5401 0.9542 0.9792 0.9652 0.9440 0.7146 0.8227 0.5723
f26 0.3665 0.1837 0.7527 0.0436 0.4418 0.1603 0.8192 0.3007 0.7148 0.1346
*Negative value refers to the reverse quality or questionable answer
To further detecting the priority of service attributes
and providing effective service management decision-
making for logistics enterprise, it necessary to use some
Kano index to carry on the analysis. The logistics service
attribute domain is divided into four quadrants based on
two dimensions: Kano satisfaction index and Kano im-
portance index. The horizontal dimension shows the de-
gree of satisfaction of a quality attribute, and the vertical
dimension shows the importance level of the quality at-
tribute. As shown in Figure 3, is the average of the r
importance index of all the express service attributes and

is the average of the satisfaction index. According to
different levels of satisfaction and importance, targeted
services management decisions are proposed.
For quadrant , it is known as the Ⅰ“Care-free” area.
C
ustomers’ perception of satisfaction index and impor-

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of
Express Delivering Industries in China
49
Table 6. Discovery of different customer groups’ knowledge.
Service attributes College students (36) Individual
workers (22)
Private
Enterprise (13)
State Owned
Enterprise (12) Overall (83)
f1 I I I M I
f2 I M I M M
f3 I M M M M
f4 I M A M M
f5 A A A A A
f6 A A I M M
f7 I I I M I
f8 I I I M I
f9 I O I O O
f10 I I I M I
f11 M M M M M
f12 I A I A A
f13 O O O M M
f14 A A I M M
f15 O M O M O
f16 I I I I I
f17 M M M M M
f18 M M M M M
f19 I O I O O
f20 M M M M M
f21 O O O M O
f22 I M I M M
f23 I I I I I
f24 I I I I I
f25 O O O O O
f26 I M I M M
tance index towards service attributes in this region is not
high. So the enterprises do not need to spend more re-
sources to focus and improve these service attributes. As
can be seen from Figure 2, the service attributes f8, f17,
f22 is in this area, and f8 is also an indifferent quality ele-
ment, so the enterprise does not need to care about the
service attribute f8.
For quadrantⅡ, it is called “Surplus” area. Customers’
perception of satisfaction index is high and perception of
importance degree isn’t high towards service attributes in
this region. If it is necessary to cut service costs, these
are the attributes that can be eliminated without incurring
a significant negative impact on the customer satisfaction.
Service attributes f1, f5, f7, f10, f16, f23, f24 are most of the
indifferent quality attributes is in this area. This is con-
sistent with the fact. For the customer, he certainly re-
gards the quality attributes that he does not care about as
unimportant quality attributes. However, not all of the
indifferent quality elements are in the area.
For quadrant Ⅲ, it is called “Excellent” area. The ser-
vice attributes located in this area are those that custom-
ers considered to be important, and for which the per-

Analysis of Logistics Service Attributes Based on Quantitative Kano Model: A Case Study of 50
Express Delivering Industries in China
Figure 3. Decision matrix based on the index of quantitative Kano model.
formance is satisfactory to customers. Retention of cus-
tomers requires that performance in these attributes be
continued. From the empirical results, it can be seen that
the service attributes f3, f4, f13, f14, f15, f19, f20, f25 are in the
region, most of these service attributes are must-be qual-
ity and one-dimensional quality elements.
For quadrant Ⅳ, it is called “to be improved” area.
The service quality attributes listed in this area are those
considered as important to customers but for which the
performances have not met with expectation. The logis-
tics company must focus on these attributes and make
improvements immediately. It can be seen from the em-
pirical results that the service attributes f2, f6, f13, f18, f26,
witch perceived as must-be quality elements, f9, f21 witch
regarded as one-dimensional quality elements and f12
perceived as attractive quality elements are in the region.
6. Conclusions
In order to solve the problems of Kano model’s quali-
tative analysis and subjective classification, a quantita-
tive Kano model is set up. By the building of Kano satis-
faction index and importance index, an objective classi-
fication method is proposed. Then a logistics service at-
tributes detection model based on quantitative Kano
model has been established to identify the attractive
quality elements, the must-be quality elements, the one-
dimensional quality elements, the indifferent quality ele-
ments and the reverse quality elements of logistics ser-
vice attributes. Based on Kano satisfaction index and
importance index, the state of logistics service attributes
is distinguished and the improvement decision-making
criteria of logistics service based on quantitative Kano
model are proposed. Enterprises must set up appropriate
priorities according to the different service attributes in
the process of developing or improving logistics service
quality. The quantitative Kano model set up in this article
enables logistics enterprises to obtain much more valu-
able information about customer needs. It is not only a
useful practical tool for industries, but it is also a theo-
retical model for academic research.
There are some shortcomings in the preliminary dis-
cussion about the construction problem of the quantita-
tive Kano model, such as the determination of threshold
value of classified rule needs to go a step further research,
and the resources constraint should be considered on the
analysis process of the decision-making of logistics ser-
vices detection, which will serve as a future direction for
future research.
7. Acknowledgements
This research was supported by Social Science Fund of
MOE under Grant 09YJA630054, 10YJA630143.
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