The Effect of Innovation Environment on the Equipment Manufacturing
Industry in China
Xu-sheng Chen, Hong-qi Wang, Yu Gu
School of Management, Harbin University of Science and Technology, Harbin, China
(zhshkslw@163.com)
Abstract – With the increasing impact of innovation
policy, external factors affecting the industry become
important in enhancing the innovation ability of firms. This
paper analyzes the influence of innovation environment in
the development of new products, promotion of technology
innovation, and in the increase in market share. First, the
paper establishes a corresponding evaluation index system
according to the external factors affecting industrial
innovation. Second, the paper demonstrates the performance
of innovation environment through scores obtained from
super- efficient data envelopment analysis (DEA), and
distinguishes the degree of effects of the different factors
through composite DEA. The results show that innovation
support for science and technology is the most significant
factor in innovation environment, and that the impact of
government support on industrial innovation is the least
significant. Finally, the study suggests countermeasures to
improve industrial innovation.
Keywords - Effect, equipment manufacturing industry,
innovation environment, super efficiency DEA
I. INTRODUCTION
Technological innovation of competitors, labor
turnover among enterprises, and the increasing market
demand have all enabled innovative behaviors to diffuse,
and various factors such as capitals, personnel and
equipments etc. to recombine within the industry,
promoted changes of production and profit distribution,
and resulted in the emergence of new industries [1].
However, industrial innovation marks the result of mutual
influence among enterprises, and the disordering trend
exists in product selection, market development and
technological industrialization in the innovative process.
Innovative risks not only come from internal enterprises,
but also serve as result of mutual influence among
innovative activities of enterprises [2]. External
environmental factors, for instance, governmental
policies, financial environment, organizational innovation
and market variations, can pose material influence on
innovative performance of the industry.
The role of government in industrial innovation
process is attracting increasing attentions, the reason of
which lies in that on one hand, government of every
____________________
Sponsored by National Nature Science Fund Project (70773032),
Humanities and Social Science Research Youth Fund Project of Ministry
of Education in China（10YJC790027）,Chinese Postdoctoral Science
Foundation（20110491099）,Heilongjiang Province Postdoctoral Fund
Project（LBH-Z10112）
nation has gradually realized that strategic industry has
significant influence on long-term development of
countries during international competition; on the other
hand, it is difficult on average for enterprises to bear
innovative risks, which calls for governmental support in
capitals and policies. Aiming at innovation conditions,
development trend and existing problems in the field of
precision engineering, Watanabe emphasizes that
innovation efficiency can be improved through
formulating, researching and developing technology
roadmap [3]. Despite that compared with larger
enterprises, small companies prefer innovation, greater
innovation risks are in existence due to limitations of
scales and technological conditions. Wallsten establishes
an equation with several unknowns to test and verify
relationship between innovation activities of small
enterprises and funding [4]. In addition, as maker of macro
control policies in the market, government can coordinate
differences between industrial development and social
demands according to innovation orientation and
requirements on environmental protection of the country
or region it locates.
It is the main purpose of enterprises to adapt to
market changes, improve product diversity and reduce
production cost in innovation process, and technological
industrialization time enjoys direct influence on
occupying market in early phases, which enables
enterprises to attach importance to short-term benefits to
improve profits, and ignore technology accumulation as
well as systematic training of R&D personnel, thus results
in the difficulty in realization of technology innovation by
leaps and bounds [5]. R&D of scientific institutions as well
as teaching mode of colleges and universities that is
catered to innovation can supplement enterprises from
realizing technological breakthrough and forming
innovation teams of high qualities [6]. By applying triplehelix theory in an earlier phase, some Scholar propose
that a synergetic mechanism among administrative
departments, R&D establishments and enterprises in the
nation can be formed in biology, information and
communication technology etc [7][8]. In accordance with
analysis on formulation process of lead-free solder
standard in developed countries of America, Europe and
Japan etc, Masaru designs a cooperative network
involving universities, industries and public departments,
and determines characteristics of structures of cooperative
network in promoting implementation of new technology
standards and improving innovation efficiency [9].
Venture capital has become main source of enterprise
innovation fund in developed countries. Especially that in
knowledge-intensive industries, venture capital enjoys a
close relationship with patent growth, technology
efficiency and expansion of enterprise scales. Venture
capital has become part of innovation strategies in
enterprises [10]. It is indicated in empirical study on effects
that support venture capital policies of America since
1979 that increasing of venture capital can promote patent
growth materially [11]. Based on the research of corporate
relationship regarding German technical innovation and
Investment Company, Weber structures conception of
“relational fit”, and puts forward that cooperation of both
parties marks the key to promote knowledge
transformation and innovation and improve organizational
efficiency [12]. According to analysis on innovation
models in recent 50 years, Engel proposes a new
commerce innovation model formed through integration
of entrepreneurial spirit, technology variation and venture
capital, whose fundamental innovation, in general,
surpasses technology scope and changes management
method of supplying chain [13].
FDI can not only provide capitals to development of
host countries, but also promote economic development
through increasing industrial employment capacity and
foreign trade volume. The aforesaid influence subjects to
factors of technical compatibility, market gap, and
technology absorption capacity of enterprises in host
country. C demonstrates that negative influence of
technical overflow occupies a dominant status when there
is excessively big gap in technology, and motivation of
technology innovation is in proportion to network
externality [14]. Moreover, Wang Zong-Ci constructs twostage Cournot model, and analyzes conditions and
features of reverse overflow effect in FDI technology
diffusion [15]. There are technological differences between
foreign enterprises and those of host nations; as a result,
externality and knowledge distribution of technology
results in that FDI exerts direct influence on overflow of
industrial innovation technology in host nations [16].
II. METHODOLOGY
1) Performance Evaluation Based on SE-DEA:
DEA often be used to describe the behavior of innovation,
and analyzes the various production input and output of
between the innovation process. In practice, each
enterprise prior do not know their own revenue function,
and just having the observation of input-output data. So
the production possibility set can be defined as followed.

T j  ( x j , y j )

y j  j  y j,   j  1,  j  0, k  1,, s

xj  j  xj,
k 1
k 1
k 1

s
k
k
s
k
k
s
k
n

 max   j y j
j 1

 s.t f j ( xi )  

n
 xj 

j 1
 x j  0, j  1,, n

(1)
Following linear programming problem can be used
to instead of problem (P ) .
n

max   j y j

j 1

s

s.t. x kj kj  x j

k 1
 s
  y kj kj  y j , j  1,  , n
 k 1
( P)
s
sj  1


k 1

s

xj  


j 1

k
  j  0, k  1,  , s
 x  0, y  0, j  1,  , n
j
 j
(2)
Dual problem of the problem (P ) refers to (3).
n

min u      0j

j 1

k
k
0
 x j   j y j   j  0, k  1,, s
( D)
u 


 j   j , j  1,, n
 0
 j  0, u  0,   0,  j  0, j  1,, n
(3)
We call (P ) and (D ) are non-parameter DEA
model. Thus, SE-DEA model can overall distinguish
efficient DMU which value of  is 1 by CCR. The SEDEA model can be expressed in Figure 1. DUMS of
efficient DEA are A, B, C, and D, DUM E is inefficient
point. Among compared in efficient point, super efficient
value can be obtained based on new best frontier, which
the point of super efficient value be excluded from set of
decision-making, for example super efficient value of
point C can be calculated based on changed best frontier
of ABD, its efficiency is OC′/OC ＞ 1. Since the best
frontier to invalid point is still ABCD, Efficiency of
efficiency value is unchanged.
input2
A
k
Effective production frontier is the corresponding
surface of the production function y  f (x) . The
programming problem (P) equivalent refers to (1).
E
B
C′
C
D
input1
Fig. 1. SE-DEA model
2) Index Comparison Based on Composite DEA: DMU
can be a factor in the effectiveness of the unit vector for
the component  ( D )  (1 ( Di ),...,  n ( Di )) T , which is
obtained using the above model. Remove the first species
with di said output indicator set. Refers to the use of in di
DEA method, find the effectiveness of various policy unit
of coefficient vector obtained  ( Di ) , and can prove
capacity of science and technology, so it is removed from
the samples.
TABLE I
DUM OF MANUFACTURING INDUSTRY
DUM
Industry
1
Processing of Food from Agricultural Products
2
Manufacture of Foods
3
Manufacture of Beverage
4
Manufacture of Tobacco
and are adapted to analyze the information of variation
regularity. For the invalid DMU, the input index which
affects its performances value can be studied, firstly,
Invalid vectors  ( D j 0 ) should be calculated, thus, new
5
vectors that S i  ( j 0 ( D)   j 0 ( Di )) /  j 0 ( Di ) is defined, For
total influence on invalidation of DUM is computed
according to (4), the index which has bigger influence
score regard as main factor.
8
Manufacture of Textile
Manufacture of Textile Wearing Apparel, Foot ware
and Caps
Manufacture of Leather, Fur, Feather and Its Products
Processing of Timbers, Manufacture of Wood,
Bamboo, Rattan, Palm, Straw
Manufacture of Furniture
that  ( D)   ( Di ) . Obviously, indicators are related to the
efficiency value of DMU, the main influence index can be
identified through efficiency change, the scores of  ( Di )

Si   ( j 0 ( D)   j 0 ( Di )) *100 /  j 0 ( Di ) 
6
7
9
10
Manufacture of Paper and Paper Products
11
14
Printing, Reproduction of Recording Media
Processing of Petroleum, Coking, Processing of
Nucleus Fuel
Manufacture of Chemical Raw Material and Chemical
Products
Manufacture of Medicines
15
Manufacture of Chemical Fiber
16
Manufacture of Rubber
17
Manufacture of Plastic
18
Manufacture of Non-metallic Mineral Products

12

3) Setting up evaluation indexes: According to the
DEA model, the paper establishes the index system of
inputs and outputs, there is four input indexes, which are
government support degree(X1), financial environment
(X2), service capacity of science and technology (X3), and
degree of market opening (X4). There are three output
indexes, which are the ability to development product
(Y1), level of profit of new product (Y2), and level of
technology innovation (Y3). The formulas are expressed as
followed.
X1= government funds for S&T activities (unit:
10000yuan);
X2= loans from finance institutions for S&T activities
(unit: 10000yuan);
X3= Intramural expenditures on S&T activities in
R&D institutions by industry (unit: 10000yuan);
X4= gross industrial output value of joint ventures/
gross industrial output value;
Y1= projects of new product development / projects of
scientific and technological;
Y2= sales revenue of new products enterprises/ sales
revenue of products in enterprises;
Y3= the number of invention patents in enterprises.
3) Data source: The data of empirical research are
taken from China Statistical Yearbook and China
Statistical Yearbook on Science and Technology (2009);
according to sample requirement of DEA model, 28
industry of manufacturing industry have been selected,
and data of sample is listed in Table Ⅰ.
Moreover, Other Countries have not proposed the
concept of equipment manufacturing industries except
China. This paper defined former industries which include
DUM21 to DUM27 in Table Ⅰ . Industry of culture,
educational and sports goods has not data of service
13
19
Manufacture and Processing of Ferrous Metals
20
Manufacture and Processing of Non-ferrous Metals
21
Manufacture of Metal Products
22
Manufacture of General Purpose Machinery
23
Manufacture of Special Purpose Machinery
24
Manufacture of Transport Equipment
25
Manufacture of Electrical Machinery and Equipment
Manufacture of Communication, Computer, Other
Electronic Equipment
Manufacture of Measuring Instrument, Machinery for
Cultural and Office Work
Manufacture of Artwork, Other Manufacture
26
27
28
III. RESULTS
A. Result of Efficiency by CCR DEA
Scores and rank of efficiency by CCR DEA is listed
in Table II.
TABLE II
RESULT OF EFFICIENCY BY CCR DEA
DUM
scores
rank
DUM
scores
rank
DUM1
0.1019
28
DUM15
0.8000
13
DUM2
0.2631
24
DUM16
1.0000
1
DUM3
0.8259
12
DUM17
0.6264
17
DUM13
0.3411
0.5454
0.5454
0.2766
DUM4
1.0000
1
DUM18
0.2339
25
DUM14
0.3356
0.3802
0.3802
0.2017
DUM5
0.5377
19
DUM19
0.8401
11
DUM15
0.8000
0.7334
0.3724
0.8000
DUM6
0.7700
15
DUM20
0.4300
20
DUM16
1.6162
1.7809
0.4546
1.7758
DUM7
1.0000
1
DUM21
1.0000
1
DUM17
0.6264
0.6105
0.2844
0.5779
DUM8
0.2169
26
DUM22
0.3583
22
DUM18
0.2339
0.2339
0.2082
0.1963
DUM9
1.0000
1
DUM23
0.3506
23
DUM19
0.7201
0.8401
0.7557
0.8328
DUM10
0.8732
10
DUM24
1.0000
1
DUM20
0.4300
0.4300
0.2708
0.3644
DUM11
0.7144
16
DUM25
1.0000
1
DUM21
1.3531
1.1333
0.2097
1.3531
DUM12
1.0000
1
DUM26
1.0000
1
DUM22
0.3095
0.3583
0.3583
0.2661
DUM13
0.5454
18
DUM27
0.1613
27
DUM23
0.3036
0.3506
0.3506
0.2554
DUM14
0.3802
21
DUM28
0.7916
14
DUM24
1.3062
1.0249
0.3715
1.3062
DUM25
1.0402
1.0709
0.7419
1.0106
DUM26
6.7431
2.2468
7.1645
7.1645
DUM27
0.1613
0.1521
0.1163
0.1613
DUM28
0.7916
0.7259
0.1165
0.6059
B. Result of Efficiency by SE-DEA
Scores and rank of efficiency by SE-DEA are listed in
Table III.
TABLE III
TABLE V
RESULT OF EFFICIENCY BY SE-DEA
SUM OF CHANGED RATIO
DUM
scores
new rank
DUM
scores
new rank
DUM4
100.7284
1
DUM21
1.3531
7
DUM7
3.5720
3
DUM24
1.3062
8
DUM9
1.5419
6
DUM25
1.0709
9
DUM12
1.5956
5
DUM26
7.1645
2
DUM16
1.7809
4
C. Result of Index Comparison Based on Composite DEA
The efficiency scores of DUM by composite DEA are
listed in Table IV;  ( Di ) represent the efficiency scores of
remove i index. Sum of changed ratio (  S )is listed in
i
Table V according to formula (4), which comparison to
equipment manufacturing industry (DUME).
TABLE IV
CHANGED EFFICIENCY SCORES BY COMPOSITE DEA
 j 0 ( Di )
X1
X2
X3
X4
DUM1
0.0479
0.1019
0.1019
0.0947
DUM2
0.2631
0.2499
0.1274
0.2456
DUM3
0.8259
0.5740
0.2048
0.8259
DUM4
100.7284
100.7284
100.7284
5.8697
DUM5
0.5140
0.5377
0.2564
0.3822
DUM6
0.7683
0.7700
0.4176
0.5246
DUM7
1.8064
3.5720
2.8751
3.5720
DUM8
0.2139
0.2169
0.1845
0.1966
DUM9
1.5419
1.5419
0.3189
1.5419
DUM10
0.8732
0.8498
0.2037
0.8251
DUM11
0.7140
0.7142
0.2190
0.5415
DUM12
1.5956
1.5956
0.1812
1.5956
S
X1
X2
X3
X4
DUME
40.4491
271.7694
879.8325
77.8929
IV. DISCUSSION
Through the calculation of the equipment
manufacturing industry efficiency value, the results
indicate that the efficiency value of DUM21, DUM24,
DUM25and DUM26are reach 1.The efficiency value of
DUM22 and DUM23 are located near 0.35, ranking in the
entire manufacturing sector are 22nd and 23rd. The
efficiency value of DUM21 is 0.1613, the position of its
innovation efficiency in the entire manufacturing by
comparison, ranking the 27th. The Data envelopment
analysis can not give fine distinction while the efficiency
values is 1, so the saving situation of input elements are
adjusted by SE-DEA method, and the samples which
efficiency value not to reach 1 remains. After adjustment,
innovation efficiency value of DUM26 is the highest,
about 7.1645. Through the analysis of the influence of
innovation environment of the equipment manufacturing
by composite DEA, we concluded the supporting ability
of science and technology most significant impact on
industrial innovation, followed by financial environment,
Government support and the degree of market opening on
the industrial innovation effect is weak.
V. CONCLUSION
This paper analyzes the external influences of
industrial innovation on the equipment manufacturing
industry in China using an index system. The index
system includes government support, financial
environment, support for science and technology, and the
degree of market opening.
In the manufacture of communication, computer, and
other electronic equipment with higher technology
support capabilities and greater market-opening degree,
the corresponding output indicators relating to new
product development and the number of patents in the
manufacturing industry are the highest. Hence, the
efficiency score in the equipment manufacturing industry
is the highest, which reflects the high-input and highoutput feature of innovative influence. The efficiency
values of manufacturing measuring instruments and
machinery for cultural and office work are lowest because
new sales income are lower in proportion compared with
the main business. The percentage of industries with an
efficiency value of 1 belongs to the equipment
manufacturing industries at 57.14%, and the proportion of
efficiency value of other manufacturing industries is
23.81%. The efficiency value is significantly lower in the
other manufacturing industries compared with that in the
equipment manufacturing industry.
Based on empirical research, the impact of innovation
support for science and technology is the most significant.
Thus, enterprises should promote mechanisms of
cooperation with research institutes and universities, and
use their respective advantages to develop new
technologies and new products.
The equipment manufacturing industry has always
been the focus of government support, but the influence
of government support on the innovation of the equipment
manufacturing industry is the weakest. This weak
influence shows that government investment efficiency
should be raised. Establishing a mechanism for financial
classification, developing a number of promising projects,
implementing tilt funds for key projects, and improving
the dissemination of knowledge infrastructure are the
main measures to improve support efficiency.
The financial environment has a lower influence on
the innovation of the equipment manufacturing industry.
The low influence shows that the financial system
supporting industrial innovation is undeveloped in China.
Enterprises should attract social capital using multichannels such as credit, equity and bonds, circulation loan
amount, small business joint guaranteed loans, corporate
account overdraft, chattel mortgage, and so on.
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