CHEMICAL ENGINEERING TRANSACTIONS
VOL. 51, 2016
A publication of
The Italian Association
of Chemical Engineering
Online at www.aidic.it/cet
Guest Editors: Tichun Wang, Hongyang Zhang, Lei Tian
Copyright © 2016, AIDIC Servizi S.r.l.,
ISBN 978-88-95608-43-3; ISSN 2283-9216
An Empirical Study on Servitization Improve Manufacturing
Enterprises’ Innovation Capability
Xuegang Shi*, Chunjian Yin
School of Economics and Management, Civil Aviation University of China; South College of Civil Aviation University of China
38 box, Dongli District, Tianjin City, China (300300)
360699792@qq.com
The paper built a comprehensive evaluation model with improved Grey Correlation-fuzzy, and made an
empirical research on the innovation capability of manufacturing enterprises in China. Evaluation results show
that enterprises which have taken servitization strategy get higher index value than the enterprises have not,
wherever in the enterprise innovation opportunity recognition capability, innovation process execution capability,
innovation results evaluation capability, and innovation comprehensive capability.
1. Introduction
There are many factors which influence the innovation activities of enterprises, especially in the current trend of
manufacturing servitization. In the case of innovation is increasingly becoming a system behaviour that involved
multiple subjects and combined with each other in a variety of elements, requiring companies to have a more
comprehensive and diversified innovation capability.
(Li et al., 2015) had researched 52 manufacturing companies and found that most of the companies’ innovation
efficiency is low because of their low scale efficiency. (Han and Wang, 2015) revealed that there are some
internal driving factors like collaborative innovation willingness, as well as some external driving factors, such as
the market competition pressure. (Joseph, 1982) believed innovation is the concept of a combined ability; it is a
combination of technological innovation, system innovation capability and other factors together. (George,
2004) believed that product innovation is the result of joint action by three factors: technology, product and
market space organization and management. (Yang, 2001; Liu, 2000) have done pioneering research of product
innovation and found that product innovation is a comprehensive ability that the enterprise in order to occupy the
market and gain inner profit. (Sun et al., 2010) selected 16 indicators from technology inputs and outputs, and
put forward countermeasures on how to improve the technological innovation capability of enterprises.
2. Methodology
2.1 The selection of evaluation index
From the perspective of innovation process, the paper argues that innovation capability should include three sub
abilities, innovation opportunity recognition capability, innovation process execution capability, and innovation
result evaluation capability. The innovation opportunity recognition capability refers to the ability of enterprises to
find, identify and capture/seize the innovation opportunities in changeable market environment. The innovation
process execution capability stands for the ability of enterprises transforms innovative ideas into products or
services, evading risk and creating profit for the enterprise. The innovation result evaluation capability means
the ability that enterprises make a scientific evaluation and examination for innovation, and can reflect the
innovation value accurately. The above three dimensions can be from the perspective of innovation process,
reflecting innovation capabilities of manufacturing enterprises more comprehensively and give more
consideration to the important role of customer as well as other members of the network in collaboration under
the background of manufacturing servitization.
On the basis of literature study (Zheng and He, 2000; Liu 2007), the paper combined with the theorization, the
comprehensiveness, the independence, the representative, etc. of constructing index system, based on the
DOI: 10.3303/CET1651218
Please cite this article as: Shi X.G., Yin C.J., 2016, An empirical study on servitization improve manufacturing enterprises' innovation
capability, Chemical Engineering Transactions, 51, 1303-1308 DOI:10.3303/CET1651218
1303
trend of manufacturing servitization, determining the relevant secondary indexes respectively. As shown in
Figure 1.
Figure 1: Evaluation system of enterprise innovation capability
2.2 Grey Correlation Degree analysis method to determine index weight
Using of the improved Grey Correlation Degree analysis method to operate and compute, is able to determine
the overall impact on the index system of each subsystem of any indicators for the rest indicators, and thus
determine the index weight. General steps are as follows:
For n companies to be evaluated P(1), P(2), …P(n),x1,x2,…, xm are evaluating indicators, P
(j)
xi stands the value of
indicator xi of company P
(j) were evaluated on a fixed point.
Step 1: Do dimensionless processing for sequence of indicators – object
Usually, we need to do the dimensionless processing for sequence x(p) 1 , x
(p)
2 ,…, x
(p)
m . By using standardized
dimensionless processing, change the data from x(p) 1 , x
(p)
2 ,…, x
(p)
m into φ(x) 1 , φ(x) 2 ,…, φ(x) m .
Step 2: Calculate the square correlation degree
We choose each sequence from φ(x) 1 , φ(x) 2 ,…, φ(x) m . respectively as the mother sequence, and correspondingly the
left sequence as a subsequence, then we got (m-1)×(m-1) amount of correlations and generated an m-order
Correlation Matrix Ψ(x) m .
As a sequence cannot be calculated with itself, so the elements on the diagonal in this Correlation Matrix are
empty, this has shown as follows:
1304
(1)
The ξ(x) ij denotes the degree of association between subsequence x(p) j and the mother sequence x(p) j which
represents influence degree that Xi index reacted to index Xj,
Where, j=1, 2, …, n, i≠j,
Step Three: Determine △(x) i and then get its weights a
(x)
i
Determine the △(x) i , which is evaluated as follows:
, Where i=1, 2, …, m,
(2)
The △(x) i represents an overall influence which Xi carries to other indices, and also stands for the degree of
importance in the whole index system.
Normalize for a(x) i , which is evaluated as follows:
, where i=1, 2, …, n and a(x) i is the weight of index Xi,
(3)
Meanwhile we got weight vector A(x) m =(a
(x)
1 , a
(x)
2 ,…, a
(x)
m ) of index X1, …, Xm.
Similarly, we got weight vectors of secondary indicators and first class indicators respectively, which are shown
as follows:
2.3 Calculation of Fuzzy Evaluation Matrix
We set P(j) x =(P
(j)
x1, P
(j)
x2,…, P
(j)
xm) as an Object - Index Sequence that comes from company’s (named P
(j)) statistics
about X1, X2, …, Xm, Where i=1, 2, …, m, j=1, 2, …, n.
At the same time, we got P(1) x , P
(2)
x ,…, P
(n)
x respectively.
It was assumed an optimal sequence P* X=(P
*
1, P
*
2,…, P
*
m ) , which is operated as follows:
, if index i takes the maximum value；
(4)
, if index i takes the minimum value,
(5)
Where i=1, 2, …, m;
If the individual components of the resulting sequence (P* X, P
(1)
x , P
(2)
x ,…, P
(n)
x ) have a different dimension, the
correlation coefficient cannot be calculated, and then they need to be dimensionless, as P* X, P
(1)
x , P
(2)
x ,…, P
(n)
x is
the Object - Index Sequence, we usually choose dimensionless method of range dealing. After that we
symbolize it as C* X, C
(1)
x , C
(2)
x ,…, C
(n)
x .
Now we set C* X as mother sequence, C
(1)
x , C
(2)
x ,…, C
(n)
x as subsequence, then calculate the correlation coefficient
η(x) jt . After calculating we got
( )
( ) ( )
( ) ( )
( ) ( )
−
−
−
=Ψ
X
m
X
m
X
m
X
X
m
X
X
m
21
221
112
ξξ
ξξ
ξξ
( ) ( )
≠=
=Δ
m
ijj
X
ij
X
i
,1
ξ
( )
( )
( )
=
Δ
Δ
=
m
j
X
j
X
iX
ia
1
( ) ( ) ( )( )XXXX aaaA 821 ,,, = ( ) ( ) ( )( )YYYY aaaA 821 ,,, =
( ) ( ) ( )( )ZZZZ aaaA 621 ,,, = ( )321 ,, aaaA =
( ){ }jxnji iPP ,,2,1max=
∗ =
( ){ }jxnji iPP ,,2,1min=
∗ =
1305
(6)
where, the η(x) jt ( i=1, 2, …, m, j=1, 2, …, n ） is the correlation coefficient or Superior degree of the evaluated
companies P(j) on the terms of index Xi. Therefore, we set it as Membership of Fuzzy Sets (named f(xi) ), and set
η(x) jt =r(x) ij .
So far, we have got the membership r(x) ij (where i=1, 2, …, m, j=1, 2, …, n ) of evaluated companies, and we got
the Fuzzy evaluation matrix transfer from Ux to P .
(7)
Similarly, we get Ry that transfer from UY to P ; RZ that transfer from UZ to P ; R from U to P.
2.4 Two levels of Grey Correlation Degree - Fuzzy Comprehensive Evaluation Model
By using Ax=(a
(x)
1 , a
(x)
2 ,…, a
(x)
m ) and Rx we can generated a Fuzzy Comprehensive Evaluation Model, symbol as Bx,
where Bx=(b
(1)
x , b
(2)
x ,…, b
(n)
x )=Ax⋅Rx.
We set Bx as evaluation vectors of N evaluated companies .and b
(j)
x is the index of the first class index X
(Innovation Opportunity Recognition Capability) of a company.
Similarly, we get By and BZ respectively stand for the first class index Y (Innovation Process Execution
Capability) and the first class index Z (Innovation Result Evaluation Capability) of a company.
BY=(b
(1)
Y , b
(2)
Y ,…, b
(n)
Y )= AY⋅RY
BZ=(b
(1)
Z , b
(2)
Z ,…, b
(n)
Z )= AZ⋅RZ
B=( b1, bs,…, b n)=A⋅R
3. Results and Discussion
3.1 Data sources and statistical description
We investigated 60 companies by filling out the questionnaire, which including state-owned enterprises,
collective enterprises, private enterprises, joint ventures, foreign-funded enterprises and various businesses of
all sizes, which located in Beijing, Tianjin, Shanghai, Hebei, Jiangsu and other provinces in China, and which
involve many industries, such as automobile industry, electronics, household appliances, heavy machinery,
electrical equipment, plastic products, instruments, metallurgy, cement, chemical, pharmaceutical, tobacco,
food processing industry, etc. Among the 60 surveyed companies, there are 27 enterprises implemented
servitization strategy, accounting for 45% of the total survey.
Table 1: All levels of the index weight vector
Index X x1 x2 x3 x4 x5 x6 x7 x8
Weight 0.1018 0.1285 0.1340 0.1308 0.1264 0.1301 0.1161 0.1322
Index Y y1 y2 y3 y4 y5 y6 y7 y8
Weight 0.1230 0.1310 0.1342 0.1106 0.1206 0.1299 0.1223 0.1285
Index Z z1 z2 z3 z4 z5 z6
Weight 0.1457 0.1838 0.1805 0.1799 0.1807 0.1294
Indicates X Y Z
Weight 0.3303 0.3538 0.3159
( )
( ) ( )
( ) ( ) ( )
=
X
nm
X
n
X
n
X
m
XX
X
m
XX
X
ηηη
ηηη
ηηη
η
21
222
)(
21
)(
1
)(
1211
( ) ( )
( ) ( )
( ) ( )
( ) ( ) ( )
( ) ( ) ( )
( )
T
X
nm
X
n
X
n
X
m
XX
X
m
XX
X
mn
X
m
X
m
X
n
XX
X
n
XX
X
rrr
rrr
rrr
R
=
=
)(
2
)(
1
22221
11211
)(
21
2
)(
2221
1
)(
1211
ηηη
ηηη
ηηη
1306
3.2 Data Processing and Analysis
1. Data processing
According to the actual situation of these 60 companies, we endow them score from 0 to 5, these are what all
raw data come from, and therefore it does not require non-dimensional processing. We mathematically operate
the raw data by using Matlab programming method, and then to obtain weight of index of each underlying
system and upper system, we got fuzzy evaluation matrix and the index value as well.
2. The evaluation results
It can be obtained all levels of index weight by operational programming (list in Table 1). Because of the large
data of fuzzy evaluation matrix and results, we do not list all the data in the article, but show them in Figure 2.
Figure 2: a-Scatter diagram of innovation opportunity recognition capability index
b- Scatter diagram of innovation process execution capability index
c- Scatter diagram of innovation result evaluation capability index
d- Scatter diagram of enterprise innovation capability comprehensive index
3. The analysis of evaluation results
In Figure2, “Yes” means the companies which have taken manufacturing strategy of servitization, “No” means
the companies which have not. From figure 2a, we can clearly see that companies which have taken
manufacturing servitization strategy get higher index on Innovation Opportunity Recognition Capability (average
index: 0.2758) than those haven’t taken manufacturing servitization strategy (average index: 0.1430). And the
similar results which can be seen from Figure 2b, 2c. Thus, it can be concluded that the manufacturing
servitization has a promotion effect on enhancing the Innovation Opportunity Recognition Capability, the
Innovation Process Execution Capability, and the Innovation Result Evaluation Capability. From figure 2d, we
can clearly see that companies which have taken manufacturing servitization strategy get higher index on the
comprehensive capability of enterprise innovation (average index: 0.1258) than those haven’t taken
manufacturing servitization strategy (average index: 0.0672). Thus we can know that the manufacturing
servitization has a promotion effect on promoting the comprehensive capability of enterprise innovation.
4. Conclusion
Firstly, enterprises developed unbalanced in the three capabilities, some enterprises have strong capability in
certain aspects, but not in others. Secondly, the impact of servitization on innovation opportunity recognition
capability, innovation result evaluation capability is more obvious, the impact on enterprise innovation process
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execution capability is relatively weak, and the impact of the enterprise innovation comprehensive capability is
also obvious.
Thirdly, manufacturing servitizations has become a clear trend in the development of the global manufacturing
industry. The empirical study also proves its important role in enterprises’ development. Therefore,
manufacturing enterprises should seize the opportunity combined with industry environment, product
characteristics and customer demand and other factors to expand the service business.
We hopes the research can provide some reference value in making developing innovative strategies, and
policies for enterprises and some relevant government departments.
Acknowledgments
The research work was sponsored by Research Project of Tianjin Science and Technology Development
Strategy under 15ZLZLZF00420, the Fundamental Research Funds for the Central Universities under
3122016D024, Scientific Research Fund Project of Civil Aviation University of China under 2012QD05X.
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