Efficiency Evaluation of Industry-University-Research Institute
Collaboration: a Case Study of Suzhou
Meng Dai1, Chi Wang2, Xiao-ran Hu3,Chengyang Xie4
1,4
author affiliations: Research Center for Group Economics and Industrial Organization in Southeast University, School of
Economics and Management in Southeast University Nanjing China
2
author affiliation: School of Foreign Languages in Southeast University Nanjing China
3
author affiliations: School of Economics and Management in Southeast University Nanjing China
1,4
2
3
( daimengmilam@126.com, wangchi_seu@126.com xiaoran_hu_2012@yeah.net)
Abstract In the government’s efforts to promote
technological innovation, one of the important parts is
industry-university-research institute collaboration. Suzhou
is the pioneering area of China’s economic development,
where industry-university-research institute collaboration
has played a significant role in its regional development. By
evaluating the efficiency of this collaboration in Suzhou and
analyzing its characteristics, we can scientifically formulate
the planning of the first development area-Sunan (south area
of Jiangsu province), lead the transformation of the local
and other regions and build the innovation-based economy.
By means of the data envelopment analysis method, this
paper intends to make a preliminary evaluation of the
efficiency of the industry-university-research institute
collaboration in each district in Suzhou.
Keywordsindustry-university-research
institute
collaboration, efficiency, Suzhou, data envelopment analysis
method
I.
INTRODUCTION
The
industry-university-research
institute
collaboration (IURC) is that the enterprises, universities
and research institutes (or other organizations) carry out
the risk-sharing and benefit-sharing technological
innovation collaboration, which is of their joint
development, joint contribution, and complementary
advantages, based on the development needs of
enterprises and the common interests of all parties. It’s
aimed at enhancing the innovation capability of industrial
technology under the protection of a legally binding
contract.
Since Chinese government implemented the policy
of reform and opening-up, the IURC began to flourish and
became one of the main cooperative forms of independent
innovation around the country, marked by two National
Science and Technology Conferences in 1995 and 2006.
Suzhou is the forefront of reform and opening up,
whose industry-university-research institute collaboration
has experienced more than 30 years’ development,
changing from “Saturday Engineer” to “Suzhou Leading
Talents.” Looking back to the course of Suzhou’s
industry-university-research institute collaboration, the
process of its development has gone through three stages:
Current achievement of the project “A Study of International
Transfer of Industrial Clusters and the Chain Effect of Industrial
Clusters in Chinese Enterprises’ foreign direct investment,” sponsored
by the national natural science funds
“Bud,” “Development” and “Prosperity.” Before the year
1994, generally the firms had more initiative in
participating in IURC than that of the government to
promote IURC, which was the characteristic of the “Bud”
stage-combination of industry, university and research
institute; From 1994 to 2002, the main form of IURC is
the firms’ participation under the government’s guidance,
which was the feature of the “Development” stage,
changing to the combination of industry, university,
research institute and government; After the year 2002,
the IURC gradually shifts to the pattern that under the
guidance of the government, firms act as the main body
directed by the market, which is the characteristic of the
“Prosperity”-close cooperation of industry, university,
research institute, government, financial institute and
intermediary institute.
Looking back on the road of industry-universityresearch institute collaboration over the past thirty years,
although Suzhou system has begun to take shape and
made significant achievements, there are still some
differences between the districts (county-level cities). As
a result, how to promote the coordinated development of
IURC has become a widely concerned problem. Therefore, it is of theoretical and practical significance to
make an evaluation of the IURC in each district (countylevel cities) in Suzhou.
Based on the current literature, this paper will
propose the series indexes that are suitable to evaluate the
IURC in the districts (county-level cities) in Suzhou. By
employing the Data Envelopment Analysis (DEA)
method, this paper will evaluate the present status of
IURC in Suzhou and offer corresponding policy advice.
II. CHOICE OF SERIES INDEXES TO EVALUATE
IURC
In order to make an exact evaluation of the IURC
efficiency of each district (county-level cities) in Suzhou,
it’s necessary to build (or choose) appropriate series
indexes for IURC evaluation. In current literature, there
are lots of research achievements about building the
IURC evaluation series indexes.
The series indexes of the IURC efficiency evaluation
has been a hot problem for both academic and political circles. For example, Xiuli Wang and Lijian Wang
(2009)[1] hold that the IURC efficiency can be considered
from two aspects: input and output. Zhiqing Shen and
Yihong Zhou (2010)[4] propose that we can study three
stages: pre-collaboration, mid-collaboration and postcollaboration. Yan Huo (2009)[5] argues that IURC can be
analyzed from three dimensions: input, process and
output. Zhengbin Xiao et al. (2009)[7] think that IURC
should be discussed at macroscopic and microscopic
levels. Feng Xia (2008)[10] discusses how to evaluate
IURC efficiency through the method of balanced
scorecard.
Some scholars adopted hierarchical series indexes.
For example, Jing Cao et al. (2010)[6] believe that the series indexes should be divided into two levels. The first
level is composed of five indexes: the environment, input,
operation
and
economic
effects
of
the
collaborative innovation. And each of these indexes
contains several secondary indexes. Huifang Zhao
(2010)[11] also adopts the system of the first-level and
second-level indexes.. Cuixian Zhang and Miao Li (2010)
[9]
then choose the three-level evaluation system:
target layer, rule layer and index layer.. Other scholars,
like Furong Jin and Shougui Luo, also employ the
hierarchical system (2009) [3].
In summary, these series indexes cover different
contents. Some are fitted for the evaluation of the specific
projects of the IURC, whereas others are appropriate for
the comprehensive evaluation of the IURC in certain
regions.
Based on the DEA method, this paper chooses the
evaluation frame of the series indexes, which are
composed of two layers-input and output. The choice of
the indexes has consulted the existing literature and the
data of the technology statistics in Suzhou.
From the aspect of input, this paper chooses 6
evaluation indexes: number of key laboratories,
number of public technical service platform, personnel number
of bachelor
degree or
above,
number of talents for scientific and technological
activities, project funds and funds of the firms’
technological activities.
From the aspect of output, this paper also chooses 6
indexes: number of talents selected for the
“National Thousand Plan”, number of talents selected for
the “Innovative or Venture Personnel in Jiangsu”, number
of “Suzhou Leading Talents”, number of patent licensing,
technology award and amount of the transaction in the
market of technology.
On the basis of the above indexes, this paper will
employ the DEA method to evaluate the efficiency of
IURC in each district (county-level cities) of Suzhou.
III. METHOD OF DATA ENVELOPMENT ANALYSIS
The data Envelopment analysis (DEA) is used to
empirically measure productive efficiency of decision
making units, which was proposed by American
operations researcher Charner and other researchers. It
promotes the efficiency evaluation from the single-input,
single-output, similar decision-making units (DMU) to
multi-input, multi-output ones, which has greatly enriched
the production
function
theory in
the microeconomics and its applying techniques. Meanwhile, it has
immeasurable superiority to avoid subjective factors, to
simplify algorithm and to reduce the errors. The unique
features and advantages of DEA has received a
widespread concern and quickly developed in both
theoretical research and practical application. It has
become one of the effective analytical tools and research
methods in the fields of management science, systems
engineering and decision analysis, evaluation techniques
and so on.
The DEA method is mainly to evaluate the efficiency
of N DMUs. Supposing that there are n DMUs and each
of these has m types of inputs and s types of outputs, let's
mark xij as the the inputs of the i-th input for the j-th
DMU (xij >0), yrj as the outputs of the r-th output for the
j-th DMU (yrj > 0), vi as the weight of the inputs and ur
as the weight of the outputs (i=1,2,
,m; r=1,2,
,s;j
=1,2, ,n).
When we evaluate the efficiency of the j0 -th(1 ≤
j0 ≤ n), the weight coefficient v and u are the variables
and the efficiency index of -th DMU is the target. We
can construct the C2R model shown in formula (1) (for
convenience, this paper notes (xj0 , yj0 ) as (x0 , y0 )).
max
(P𝐜𝟐 𝐑 ) s. t.
uT x
j
uT xj
uT x0
vT y0
≤ 1, j = 1,2, ⋯ , n,
(1)
v ≥ 0,
{
u ≥ 0.
Based on the above method, the definition of DEA
efficiency is:
[1].
If the optimal solution of the linear
programming (Pc2R ) ω0 , μ0 satisfy:
μ0T y0 = 1, and ω0 > 0, μ0 > 0.
Then DMU j0 is weak DEA efficiency.
[2].
If the optimal solution of the linear programming (Pc2R ）ω0 , μ0 satisfy:
μ0T y0 = 1，且ω0 > 0, μ0 > 0
Then DMU j0 is DEA efficient.
With the definition of efficiency, we can judge
DMUs’ efficiency through solving the model.
IV. THE EYYICIENCY EVALUATION OF IURC IN
SUZHOU BASED ON THE DEA METHOD
According to the above series indexes and efficiency
evaluation method, the DEA method can be applied to
evaluate the efficiency of IURC in the districts (countrylevel cities) in Suzhou. The data of inputs are shown in
the table 1 and the data of outputs are shown in the table
2. The main data in the two tables about the districts
(country-level cities) in Suzhou are from the
“Science and Technology Statistical Compendium of
Suzhou.”
District
Zhangjiagang
Changshu
Kunshan
Taicang
Wujiang
Wuzhong
Xiangcheng
Park area
New area
Chengqu
District
Zhangjiagang
Changshu
Kunshan
Taicang
Wujiang
Wuzhong
Xiangcheng
Park area
New area
Chengqu
Key
Laboratory
1
1
4
0
1
5
1
33
6
50
TABLE 1: Output Table of the Districts (Country-level Cities) in Suzhou in 2010
Public Service
Talent of Bachelor
Talent of Scientific
Project Funds
Technology
Degree or above
and Technological
(Ten Thousand
Platform
Activity
Yuan)
8
82
5765
5439
8
776
4934
3886
8
1975
24437
10070
5
191
3350
1559
4
276
16330
4570
18
632
3821
4761.4
5
140
2795
1365
17
8134
19668
30795.3
18
2721
19553
12701
36
1205
780
12511.6
TABLE 2: Output Table of the Districts (Country-level Cities) in Suzhou in 2010
National Thousand
Innovative or
Suzhou Leading
Patent
Technology
Plan
Venture
Talents
Licensing
Award
Personnel in
Jiangsu
0
11
6
3049
21
0
7
4
4242
10
6
25
19
10750
19
0
7
7
2602
1
2
2
5
14698
13
0
5
4
3567
9
0
3
2
1625
4
11
51
62
3014
16
7
18
22
2269
9
0
0
0
293
0
[1].
On the assumption that the scale benefit is
constant, the relative efficiencies of IURC in each district
(country-level cities) are as follows：
By substituting the data of the above tables into the
DEA model (1), and applying the software MaxDEA5.2
District
Funds of Firms’
Technological Activity
(One Hundred Million)
8.72
9.98
80.83
9.26
25.87
5.49
4.46
30.45
30.29
0.99
Transaction in the
Market of Technology
(One Hundred Million)
0.82
1.35
2.08
3.61
0.01
0.91
0.32
11.52
2.46
0.30
to solve it, we can get the IURC innovative efficiency
value θ of each district (country-level cities) in Suzhou.
Table 3 gives the relative efficiency value and slack
variables of inputs and table 4 is about the relative
efficiency value and slack variables of outputs.
TABLE 3: Relative Efficiency Value and Slack Variables of Inputs in the Districts (Country-level Cities) in Suzhou
s5−
θ
s1−
s2−
s3−
s4−
Changshu
1
0
0
0
0
0
Urban area
0.86
49.85
35.01
1168.70
365.67
12227.45
Kunshan
1
0
0
0
0
0
Taicang
1
0
0
0
0
0
Wujiang
1
0
0
0
0
0
Wuzhong
1
0
0
0
0
0
Xiangcheng
0.97
0.79
2.64
78.85
472.55
0
New area
1
0
0
0
0
0
Park area
1
0
0
0
0
0
Zhangjiagang
1
0
0
0
0
0
Note: the indicators of slack variables in this table rank the same as table1. (The data are kept to two decimal places.)
District
TABLE 4: Relative Efficiency Value and Slack Variables of Outputs in the Districts (Country-level Cities) in Suzhou
s5+
θ
s1+
s2+
s3+
s4+
Changshu
1
0
0
Urban area
0.86
0
0.77
Kunshan
1
0
0
Taicang
1
0
0
Wujiang
1
0
0
Wuzhong
1
0
0
Xiangcheng
0.97
0.10
0
New area
1
0
0
Park area
1
0
0
Zhangjiagang
1
0
0
Note: the order of indicators and data type in this table are the same as table 3
0
0.74
0
0
0
0
0.39
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.36
0
0
0
0
0
0
0
0
s6−
0
0
0
0
0
0
0.13
0
0
0
s6+
0
0
0
0
0
0
0.45
0
0
0
The table 3 and table 4 show that eight districts (or
country-level cities) are DEA efficient, which are
Changshu, Kunshan, Taicang, Wujiang, Wuzhong, Xinqu,
Park area, Zhangjiagang. Each of them has the relative
efficiency value θ =1and slack variables s-=0, s+=0,
showing that all of the inputs and outputs are in the best
conditions. The relative efficiency values of Chengqu and
Xiangcheng are respectively 0.86 and 0.97, both of which
are below 1, showing that the two DMUs are DEA
inefficient and input surplus or output shortage exists.
For areas of inefficient DMU, one or more input
indexes have non-zero slack variables, which are the
factors that restrict the efficiency of the IURC. The nonzero slack variables of the input indexes represent the
redundancy of an input index relative to the projection of
the effective frontier, and the non-zero slack variables of
the output indexes represent the shortage of an output
index relative to the projection of the effective frontier.
Based on the calculation of the slack variables, we could
analyze how to adjust the inefficient DMUs’ IURC.
Urban area has output shortage of indexes in
National Thousand Plan, Provincial Innovative or Venture
Personnel in Jiangsu and Suzhou Leading Talent. When
inputs are constant, the shortages of the three indexes are
0.77, 0.74 and 0.36. On the other hand, urban area has
redundancy in inputs of the number of the key
laboratories, the number of public technical service
platform, the personnel number of bachelor degree or
above, the number of scientific and technological
activities talents and the project funds. When outputs are
constant, the redundancies of the three indexes are 49.85,
35.01, 1168.70, 365.67, and 12227.45.
Xiangcheng has output shortage of indexes in
National Thousand Plan, Suzhou Leading Talent and the
funds of the firms’ technological activities. When inputs
are constant, the shortages of the three indexes are 0.10,
0.39 and 45 million. On the other hand, Xiangcheng has
redundancy in inputs of the number of the key
laboratories, the number of public technical service
platform, the personnel number of bachelor degree or
above, the funds of the firms’ technological activities and
the funds of the projects. When outputs are constant, the
redundancies of the three indexes are 0.79, 2.64, 78.85,
472.55, and 13 million.
[2].
Three types of IURC efficiency in Suzhou
and their scale benefits.
Similarly, by substituting the original data into the
DEA method, we can get the result of the three types of
IURC efficiency shown in table 5.
TABLE 5: IURC Efficiency and Scale Benefit in the Districts in Suzhou
TechnoloPure
Scale
Returns
District
gical
Technologi Benefit
to
Efficiency
cal
Scale
Efficiency
Changshu
Urban area
Kunshan
1
0.86
1
1
1
1
1
0.86
1
Constant
Growing
Constant
Taicang
Wujiang
Wuzhong
Xiangcheng
New area
Park area
Changshu
1
1
1
0.97
1
1
1
1
1
1
1
1
1
1
1
1
1
0.97
1
1
1
Constant
Constant
Constant
Growing
Constant
Constant
Constant
Note: the data are kept to two decimal places
Table 5 shows that all the DMUs except Chengqu
and Xiangcheng are in the effective condition in the
aspects of technological efficiency and scale benefit. And
all the DMUs are in the effective condition in terms of
pure technological efficiency. Moreover, except that
Chengqu and Xiangchegn are on the stage of increasing
returns to scale, the other DMUs are on the stage of
constant returns to scale.
V. CONCLUSIONS
Based on the existing literature, this paper chooses
series indexes of efficiency evaluation, which are suitable
for Suzhou’s IURC, and employs DEA method to
evaluate the efficiency. Through analysis, this paper has
found that at the macroscopic level Suzhou city has made
significant achievements in IURC. As for the districts
(country-level cities) in Suzhou, except urban area and
Xiangcheng, their DMUs have reached the DEA efficient
conditions. However, from the perspective of absolute volume of input and output, there is still room for
improvement for several DMUs which have reached the
DEA efficient conditions. The two DMUs which have not
reached the DEA efficiency are in the state of increasing
returns to scale. There is still much to be enhanced, which
requires us to properly regulate the input resources
and output capacity and strive to achieve the DEA
efficient condition.
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