Evaluation of Regional Innovation System: Empirical Studies of China

Evaluation of Regional Innovation System: Empirical Studies of
China
LIU Shuguang 1, 2 SONG Deyong 1
1. School of Economics, Ocean University of China, Qingdao China, 266071
2. Institute of Marine Development, Ocean University of China, Qingdao, China, 266005
[email protected]
：
Abstract Regional innovation system is among the hot topics in economic geography and regional
economics since 1990s, while the quantitative evaluation of regional innovation system has been paid
more attention in 2000s. With the latest development of regional studies, new factors such as the
relationships of local buzz and global pipelines, and new perspectives of user-led innovation and
innovation ecosystem, bring tremendous changes in regional innovation researches. The aim of the
paper is to comment about the already existed methods and also the research results on regional
innovation in the last decade. Then authors of the paper discuss two cases of provincial and city level
innovation system evaluations in China based on different evaluation models worked out according to
theoretical approaches in recent years. As a result, the paper shows that the major conclusions are still
coincided with actual situations of China at present, therefore it is necessary to adhere to the traditional
evaluation models, but new factors such as global impacts, and new methods such as ecosystem
perspectives, should be integrated into regional innovation studies.
Keywords: regional innovation system; evaluation models; cases of China
1 Introduction
The concept of regional innovation system (RIS) is taken into serious consideration just fallowing
the academic study on national innovation system of Freeman (1987) and Lundvall (1992). Porter (1990)
has done the revolutionary approaches to regional innovation system through his multinational case
studies on regional /national level competitive advantages. Cooke et al. (1996) conducts a detailed
survey on the concepts of RIS. He points out that RIS is a group of geographically adjacent enterprises,
R & D institutes and universities which enhance and help produce innovation. Wiig (1999) argues that
the broad sense of RIS should also include the local governments and local services such as financial
business institutes. Based on the review of RIS concepts and relative theoretical approaches, we can
summarize the fundamental connotations as: (1) it has certain spatial spheres with open boundaries; (2)
enterprises, R&D institutes, universities, local governments and services are regarded as the major units
of innovation; (3) different innovation units connects each other through networking and form RIS
structure; (4) innovation units produce and help produce innovation through self-organization and
interactions with outer innovation context, therefore impact on their own region and outer regions; (5)
the innovation process continues through positive self-organization and inside-outside interactions, and
help boost the regional sustainable development.
The research of regional innovation system has developed from pure concept into actual systems.
Both outer factors and inner mechanisms have considered in recent literatures. The outer context,
especially the global context, has been paid attention as the important factors of regional innovation
system, although the major focus is still upon the learning mechanism in the local milieu (Cooke, 1996;
Isaksen 2001). Global pipelines are introduced into regional development studies in recent years
catering for the rapid development of web-based knowledge transmitting over multi-scale spaces.
Bathelt, Malmberg and Maskell (2004) pointed out the dynamic interactions between local buzz and
global pipelines, stressing the increasing importance of outer impact on local regional innovation
activities. Bathelt (2008) further points out that local buzz and global pipelines are inter-changeable in
certain local-global factors interaction circumstances such as forums. Yeung 2008) argues that the
global production network (GPN will link with some local innovation systems as strategic coupling
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relations in forms of international partnership, indigenous innovation and production platform. The
research of user-led innovation help geographers consider the important contribution of local consumers
on regional innovation in knowledge-intensive industrial clusters (Aoyama, et al, 2007; Malerba, 2007).
Innovation ecosystem is another trend of regional innovation research in recent years. Tateo Arimoto
(2006) works out an input-output conceptual model to describe the sophisticated mechanism of regional
innovation factors. While European Institute of Interdisciplinary Research (EIIR, 2006) regards
innovation ecosystem will have to be combined with the elements of expertise, chain and cluster,
outsourcing, and local services.
As for the quantitative evaluation of regional innovation system, it will be of difficulties partly
owing to the unclear definition of the concepts and diversified perspectives of research. The aim of the
paper is to retrospect the researches of innovation evaluation models and empirical studies of provincial
and city level regional innovation system in recent years (Liu Shuguang, et. al, 2001, 2003a; 2003b;
2004;2006), and discuss the possible improvements through checking with new trends of regional
innovation researches mentioned above.
2 Evaluation of provincial level innovation situation in China
2.1 Establishment of the Evaluation Model
Based on the research result of regional innovation system, the situation of the system can be
initially described with five aspects of initial indexes (Liu Shuguang, Chen Cai, 2003), and they evolve
into five indicators, through evaluation-oriented index integration, the five indicators will be summed up
into two higher level indicators of system scale and quality, and these two indicators will be generalized
as final indicator of innovation total situation. (See Figure 1)
Figure 1 Evaluation Model of Regional Innovation System Situations
2.2 The Results of the Study
The paper chooses the data from published following regions as the subjects for the case study: three
municipalities, three provinces in eastern economic path, three provinces in middle economic path, and
three provinces/regions in western economic path, the data are sources from several published books
and statistics handbooks around 1998-2000(Liu Shuguang, Chen Cai, 2003). Table 1 and Figure 2 show
the final results of the RIS case study of selected regions in China. The results are acceptable and have
been vindicated with other research results at the same period (The Group of Science & Technology
Development Strategy of China, 2004).
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Table 1 Result of RIS Case Study of Selected Regions in China
Selected
Regions
Beijing
Tianjin
Shanghai
Shandong
Zhejiang
Guangdong
Jilin
Henan
Hunan
Shaanxi
Xinjiang
Yunnan
Scale of
Input
100
25
78
51
20
43
21
35
28
46
9
12
Scale of
Output
71
19
51
54
28
39
16
26
24
28
5
13
Milieu
Transition
100
33
54
10
7
9
15
8
9
29
9
7
Inner
Operation
73
47
61
37
57
56
43
29
37
37
29
43
Outer
Impact
87
80
93
45
70
100
56
64
59
51
62
60
Total
Scale
86
22
65
53
24
41
19
31
26
37
7
13
Total
Quality
87
53
69
31
45
55
38
34
35
39
33
37
Total
Evaluation
87
38
67
42
35
48
29
33
31
38
20
25
Figure 3 Regional Innovation Situations of Selected Regions in China
3 Evaluation of City Level Innovation Capacities in China
3.1 Establishment of Innovation Capacities Model
Cities are regarded as the centers of regional innovations, and they differ in certain innovative
factors at the same time, more micro level factors looms large while they are hidden in macro factors
when studying vast regions. Based on the researches city or urban regional innovation (Liu Shuguang,
2004; Liu Shuguang and Song Deyong, 2006), the paper works out the model for the evaluation of city
innovation capacities as follows. (See Figure 3)
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Figure 3 Evaluation Model of City Innovation Capacities
3.2 Factor Analysis of Evaluation Indicator City Innovation Capacities
The paper selects 18 important cities in China as samples of city level innovation capacities
evaluation, and digs out the data base from diversified open sources and reviews (Liu Shuguang and
Song Deyong, 2006). Table 2 and Figure 4 show the result based on the factor analysis procedure via
SSPS, and it is accepted by the experts as they assess the report of the relative program in 2006.
Table 2 Result of Innovation Capacities of Selected Cities in China
Component
City
Environment
Knowledge
Score
No.
Score
No.
Score
No.
Score
No.
Score
No.
Score
No.
12
16
11
6
8
7
3
5
10
14
9
13
4
1
2
18
17
15
0.3457
-0.0264
-0.5641
0.2042
-0.4386
-0.5602
-0.1916
0.1055
3.7189
-0.5588
-0.6831
0.1836
-0.7504
0.1544
-0.4310
0.1848
0.0251
-0.7180
2
9
15
3
12
14
10
7
1
13
16
5
18
6
11
4
8
17
-1.0106
-0.4019
-0.1146
-0.1822
-0.5399
-0.1917
3.7017
-0.0325
0.0452
-0.0031
0.0331
-0.0410
-0.4516
-0.6206
0.6549
-0.1134
0.1009
-0.8326
18
13
10
11
15
12
1
7
4
6
5
8
14
16
2
9
3
17
-0.2937
-0.9605
-1.2559
-0.5224
-1.0343
0.4876
0.1642
0.3482
0.7101
0.9995
-0.1072
-0.5702
0.4669
-0.2235
0.7325
-0.1844
-1.5274
2.7703
12
15
17
13
16
5
8
7
4
2
9
14
6
11
3
10
18
1
-0.2506
-0.0660
-0.3569
-0.3630
-0.7984
0.0277
-0.4098
-0.5622
0.5547
0.3322
3.7103
-0.2956
0.3419
-0.0252
-0.1457
-0.5642
-0.2600
-0.8690
9
7
12
13
17
5
14
15
2
4
1
11
3
6
8
16
10
18
0.6043
2.1000
0.9188
-0.9197
1.0400
-1.7597
0.2621
0.2311
0.1378
-0.1282
0.3286
-0.9328
-0.9672
-0.0743
0.9265
-0.4877
-1.7273
0.4475
5
1
4
14
2
18
8
9
10
12
7
15
16
11
3
13
17
6
Qingdao -0.2991
Jinan
-0.5480
Harbin -0.2855
Shenyang -0.0373
Changchun -0.2178
Dalian -0.1984
Beijing 0.2703
Tianjin 0.0381
Shanghai -0.2725
Hangzhou -0.4627
Nanjing -0.2686
Wuhan -0.3947
Xiamen 0.2319
Shenzhen 3.8002
Guangzhou 0.4659
Chengdu -0.7455
Xi’an -0.5743
Ningbo -0.5021
Service
Industry
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Technology
Institution
Figure 4 Innovation Capacities of Selected Cities in China
6 Concluding Remarkst
The initial empirical researches of regional and urban level innovation systems show that: (1) there
are actual differentiations among region and cities taken general innovation factors into consideration,
innovation is the result of multi-facet interactions in the diversified local milieu; (2) scale matters when
we survey the provincial and municipal level innovation spaces respectively, and these variation come
largely from the different innovative activities in regional centers and the regional as a whole; (3) there
are endogenous input-output relations from innovative elements enhancing to innovation units and
structures, and these will also influence the innovation performances of the system.
But, if considering new trends of regional innovation system researches, the models of evaluating
regional innovation systems will have to be improved at least in outer factors, especially in global
factors. Innovation receivers as the leading force of regional innovation, will bring more changes in
factors selection and mechanism design, since most of the models are based on the hypotheses that
regions are driven by R&D actors but not users, how to count for the user-led innovation is still remain
unsolved in quantitative models. At the same time, new methods of evaluation should be added into the
already existed methods set. Artificial neutral networks, ecosystem analysis, fuzzy analysis, etc, should
be carries out based on actual survey of true factors and thorough discussion of innovation system
frameworks in different regions and/or cities.
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Author in brief: Liu Shu-guang (1966- ), male, a native of Xiajin of Shandong, is Ph. D. and professor
in School of Economics, Ocean University of China. His research interests include regional innovation
system and marine economics. Post code: 266071 E-mail: [email protected]; [email protected]
5.
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