Evaluation & Positive Study on the Competitiveness of

Evaluation & Positive Study on the Competitiveness of
Hi-tech Zones Based on Industrial Clusters
WANG Yuxin, LIU Xiaobing
School of Manage, Dalian Univ. of Techno, Dalian, China, 116024
[email protected]
Abstract: According to the basic features and components of hi-tech zone competitiveness, this paper
constructed the hi-tech zone competitiveness evaluation index system on industrial cluster. This paper
determined the optimal weights of indicators with fuzzy membership indicators score method and
G1-deviations maximization method, and then established the evaluation model for competitiveness on
G1-deviations maximization method of seventeen hi-tech zones of china. The result of evaluation
reflects the competitiveness of national high-tech zones objectively, which provides governments some
new references to enhance the competitiveness of high-tech zones.
Keywords: Industrial cluster, hi-tech zone, competitiveness evaluation, combined weight
Foreword
Hi-tech industrial clusters feature the key to the development of hi-tech zones. To appraise the
competitiveness of hi-tech zones from the angle of industrial clusters has directive significance for the
government to improve the competitiveness of hi-tech zones, cultivate hi-tech industrial clusters,
accelerate and promote the healthy, steady and rapid development of hi-tech zones.
(1) Status quo of the study on industrial cluster theory
The phrase - industrial clusters firstly appeared on the representative work of Michael E. Porter of U. S.
A. Harvard University - the Competitive Advantage of Nations, where industrial clusters are defined as
one group of industries that are connected by vertical or horizontal relations[1]. In the article – Clusters
and the New Economics of Competition, Porter holds that industrial clusters features one phenomenon
that in a specified field, a lot of enterprises with close industrial relations as well as related supporting
facilities gather together in space, and form powerful and continuous competitive advantages[2]. From
the angle of production connections, Pyke thinks that industrial clusters boasts the gathering of related
enterprises during the production course[3]. Rosenfeld considers that industrial clusters features the
channel that is established for positive commercial trading, communication and conversation with the
purpose of sharing specialized infrastructures, labor market and service, and jointly facing the
opportunities, challenges and crisis, and they are some similar, related or mutually complementary
enterprises with certain geographic boundary but are concentrated[4].
(2) Status quo of the study on competitiveness theory
The influence of the studies made by WEF and IMD on international competitiveness is the widest, and
features the representative of macro study methods on international competitiveness[5]. In the book –
Competitive Advantage, Porter puts forward the Five Forces Model from the analysis on industrial
structure[6]. And the Competitive Advantage of Nations raises that the six factors that can determine
whether one specified industry in a country has international competitiveness constitute the National
Diamond Model[7].
(3) Status quo of the study on the hi-tech zone evaluation index system
In the book- Silicon Valley Fever, American scholars Rogers and Larson conducted systematic
investigation on the starting and growth process of the Silicon Valley in U. S. A. through the qualitative
method and revealed the conditions for the Silicon Valley to form the Cohesive Economic Effect[8].
Lugar and Goldstein put forward the indexes of the successful factors of science and technology parks
from five aspects[9]. By comparative analysis between the two hi-tech zones respectively located in the
143
Silicon Valley and No. 128 Road, Boston, Saxenian pointed out that the difference between system
environment and community culture is the key making both present different development trends[10]. In
2008, the Ministry of Science and Technology newly published the Evaluation Index System for
National Hi-tech Industrial Development Zones. The representative studies of academia on hi-tech
evaluation system include the qualitative analysis made by Yi Jun and others on the development status
of 7 state-level hi-tech zones in the northeastern region from such four aspects as hi-tech zone
development degree, contribution, technical innovation and internationalization[11]; Li Lin and Chen
Xiaohong studied the competitiveness evaluation index system of hi-tech zones based on hi-tech
industrial clusters[12]. Shao Xueqing and Lu Bowen expounded the studies on national hi-tech zones in
China from a relatively systematic way[13].
The studies on hi-tech zone evaluation in other countries mainly focus on the evaluation of factors that
make related science and technology parks succeed as well as location conditions. Although the hi-tech
zone evaluation system promulgated by the Ministry of Science and Technology of China is
comparatively authoritative, it is oriented to policy evaluation. Thus, studies on the competitiveness
evaluation of hi-tech zones from the angle of industrial clusters shall be made in a deep-going way.
(4) Status quo of the study on hi-tech zone evaluation methods
I. Evaluation & study based on objective methods. Xu Chensheng analyzed and appraised the efficiency
of 53 hi-tech zones at state level with DEA method[14]. Zhang Wei and others appraised the development
conditions of 52 hi-tech development zones at state level with the factor analysis method[15]. II.
Evaluation & study based on subjective methods. Liu Kanghao and others appraised the competitiveness
of regional investment economic environment by fuzzy comprehensive evaluation[16]; Zhu Shaopeng
and others set up a set of index system that can be commonly applied in hi-tech zone evaluation by AHP,
and made comparative evaluation on the competitiveness with the hi-tech zones in Zhongguancun,
Zhangjiang and Shenzhen as examples[17].
Major problems existing in current hi-tech zone evaluation studies are as follows: the first problem is
that either methods centering on qualitative valuation, such as AHP, expert experience evaluation
method, etc., or specialized statistical methods are used in determining the index weights in each study,
with great difference in evaluation results, thus making it difficult to objectively and accurately reflect
the actual conditions. And the second problem is that there are no combined weight evaluation methods
to reflect the comparative differences of indexes and comprehensively consider the objective and
subjective information.
By synthesizing the abovementioned problems and from the angle of industrial clusters, the hi-tech zone
competitiveness evaluation index system is established, the competitiveness evaluation model based on
the combined weight process of G1-deviation maximization is set up, and positive study is conducted in
17 typical hi-tech zones in China.
1. Hi-tech zone competitiveness index system based on industrial clusters
1.1 The foundation of index system establishment
The six classical documents inclusive of Silicon Valley Fever written by Rogers and Larson [8-13] are
regarded as the typical documents for hi-tech zone competitiveness evaluation based on industrial
clusters. With reference to the Evaluation Index System for National Hi-tech Industrial Development
Zones newly promulgated by the Ministry of Science and Technology in 2008, the hi-tech zone
competitiveness evaluation system based on industrial clusters is finally set up.
1.2 The thinking on setting up the competitiveness evaluation rule layer
The competitiveness of hi-tech zones based on industrial clusters can be represented as the environment
utilization ability and evasion capability with various assets elements of clusters in hi-tech zones as
basis and the dynamic network relations among enterprises in hi-tech zones and cumulative progression
as operation mode, thus bringing powerful and continuous competitive advantage for the overall
performance of hi-tech zones in the competitions on global market. This kind of competitive advantage
144
can be shown as follows: technical innovation competitiveness, industrial cluster competitiveness,
sustainable development competitiveness and pioneering environment competitiveness.
1.3 Construction of the rule layers for hi-tech zone competitiveness evaluation system
(1) Technical innovation ability is the source and essence of hi-tech zone competitiveness. Such indexes
as scientific and technological activity expenditures, etc. in the rule layer can reflect technical input,
industrial added value in hi-tech industry can fully show technical output, and the ratio of R&D
expenditures in total income can completely represent the efficiency of technical input.
(2) Industrial cluster competitiveness is the fundamental driving force of hi-tech zone competitiveness.
Industrial concentration can be reflected by such indexes as enterprise density, etc., and industrial
gathering degree can be represented by the number of enterprises receiving service, industrial added
value rate, etc.
(3) Sustainable development competitiveness is the internal requirement of hi-tech zone competitiveness.
The sustainable development competitiveness index is set up from such three aspects as economic
development, economic radiation and international competitiveness. Economic development can be
measured by industrial added value per capita, etc.; economic radiation can be measured by taxes paid
by each person, etc.; and international competitiveness can be measured by export earnings per capita.
(4) Pioneering environment competitiveness provides powerful support to hi-tech zone competitiveness.
The rule layer for pioneering environment competitiveness includes two aspects, namely environment
input and environment output. The former is measured by governmental input in productivity promotion
center and other indexes, and the latter is measured by net profit of enterprises under incubation, etc.
1.4 Index system design principles
(1) Transverse comparability principle. Such indexes as industrial added value per capita, total revenue
per capita, export earnings per capita, etc. are sifted. For example, when the competitiveness of a hi-tech
zone in different years is appraised, the industrial added value is one comparable index. When transverse
evaluation is made on the competitiveness of different hi-tech zones, the evaluation will not true to the
original because the development scale in each hi-tech zone is distinct and the total industrial added
value in a small hi-tech zone is bound to be less. As a result, the industrial added value per capita is used
to replace the industrial added value, so as to reflect the difference of industrial added value in different
hi-tech zones in a more authentic way.
(2) Data observability principle. In order to ensure that the data are acquirable and accurate, only
quantitative indexes are used in the evaluation system. The quantitative indexes can be divided into
absolute magnitude indexes, relative magnitude indexes, etc. During the index system establishment
course, full consideration shall be given to the organic combination of such two aspects, as well as the
development scale and speed.
(3) The principle that best information shall be chosen among the indexes of the same category.
According to this principle, such indexes as the number of graduating enterprises/number of enterprises
under incubation, export earnings per capita, R&D expenditures in total income, etc. are sifted.
1.5 Setup of hi-tech zone competitiveness evaluation index system
According to the thinking on setting up the rule layers in Clause 1.3 as well as the index system design
principles in Clause 1.4. The hi-tech zone competitiveness evaluation index system based on industrial
clusters consisting of four rule layers including technical innovation competitiveness and 20 indexes in
total is constructed in the paper, as shown in Table 1.
2. Setup of hi-tech zone competitiveness model based on combined weight process
2.1 Scoring of fuzzy membership of evaluation indexes
Directive index is defined as follows: when the value is larger, the competitiveness is stronger. Direct
indexes are used for all the evaluation indexes in the paper.
It is given as below: bij refers to the score of No. i index in No. j evaluation object; Vij refers to the value
145
of No. i index in No. j evaluation object; n refers to the number of the objects under evaluation.
Based on the scoring formula of direct index[18], bij shall be:
Vij − min(Vij )
1≤ j ≤ n
(1)
bij =
max(Vij ) − min(Vij )
1≤ j ≤ n
1≤ j ≤ n
2.2 Combined weight of evaluation indexes
2.2.1 Calculation of index weights by G1 method
(1) Determining the sequence and relation of evaluation indexes by G1 method.
(2) The experts offer the rational value of the importance ratio ri between the neighboring evaluation
indexes xi-1 and xi.
(3) If the experts (or decision makers) provide the rational value of ri , the weight wi of No. i index
calculated by G1 method shall be as follows:
m
wi = (1 + ∑ ∏ri )−1
(2)
i=2
(4) Through the weight wi , we can derive the weight calculation formula of No. m-1, m-2, …, 3, 2
index as follows[19]:
wi-1=wiki i=m,m-1,...,3,2
(3)
Where, wi-1 refers to the weight of No. i-1 evaluation index calculated by G1 method; ri refers to the
rational value given by experts (or decision makers); wk refers to the weight of No. k evaluation index
calculated by G1 method.
It is given that wi z is the vector (i=1, 2, …, m) formed by the weights of evaluation indexes calculated
through G1 method, and wi z shall be as follows[19]:
No.
1
2
3
4
5
6
7
8
9
10
Table 1 The hi-tech zone competitiveness appraisal index system based on industrial clusters
Rule
Rule
Index layer
No.
Index layer
layer
layer
3
X11 Industrial added value in hi-tech industry (RMB 10
X31 Industrial added value per capita
11
yuan)
(RMB 103 yuan)
X
Ratio
of
R&D
expenditures
in
total
X32 Ratio of sales income of hi-tech products
12
X1
X3
12
income
Technical
Sustainable in total income
X
Ratio
of
number
of
hi-tech
enterprises
in
X Ratio of total assets to industrial
13
innovation
13 development 33
competitiven total enterprises in the zone
competitivene output value
X14 Ratio of personnel with semi-senior or
X34 Export earnings per capita (USD
ess
ss
14
senior professional title in total employees
103/person)
X15 Scientific and technological activity
X35 Total revenue per capita (RMB
15
expenditures (RMB 103 yuan)
103 yuan/person)
X41
Governmental
input
in
X21 Industrial added value ratio
16
productivity promotion center (RMB
103 yuan)
X42 Total assets in productivity
X2
X4
X22 Ratio of technical income in total income 17
Industrial
Pioneering promotion center (RMB 103 yuan)
cluster X23 Ratio of hi-tech industrial output value in
environment
X
Accumulative number of
competitiven total industrial output value of the hi-tech 18 competitivene 43
graduating enterprises (enterprise)
ess
ss
zone
X44 Net profit of enterprises under incubation
X24 Enterprise density(enterprise/sq. km.)
19
(RMB 103 yuan)
X25 Incubation density(enterprise/sq. km.)
20
X45 Training service (time/person)
wi z =(w1,w2,…,wm)
(4)
The feature of weighting by G1 method is to reflect the importance of index by subjective sequencing.
Larger weights will be given to important indexes.
146
2.2.2 Determination of weight by maximum deviation method
The principle for weight determination by maximum deviation method is as follows: Based on the
influence on evaluation results, if the deviation degree of index values for evaluation objects is higher,
the weights of such indexes will be larger.
As for one multi-index evaluation problem, it is given that the index value matrix after normalization is
B=(bij)m×n, where bij(i=1, 2, …, m; j=1, 2, …, n) refers to the score of No. i index in No. j evaluation
object. It is given that wi is the weight of No. i index, wi equals or exceeds 0, and meets with the unit
constraint conditions. For index i, Fij(w) is used to represent the deviation (k=1, 2, …, n) of the index
values of evaluation object j and all other evaluation objects, then [20],
m
(5)
( w) = ∑
−
F
ij
i =1
bw b w
ij
i
ik
i
Thus, for index i, the total deviation of all the evaluation objects and other evaluation objects can be
shown as follows[20]:
n
n
n
(6)
−
( w) =
( w) =
F
∑F
i
j =1
∑∑ b b w
ij
j =1 k =1
ij
ik
i
Based on the maximum deviation principle, the optimal model is set up[21].
m
n
n
max F (w) = ∑∑∑ b ij − b ik wi
i =1 j =1 k =1

w ≥0
∑ w = 1

st. .
(7)
i
m
2
i
i =1
After solution of the optimal model and normalization, the formula of weight determination by
maximum deviation method is got[21]:
n
w
i
=
n
∑∑ b − b
j =1 k =1
m
n
ij
(8)
ik
n
∑∑∑ b − b
i =1 j =1 k =1
ij
ik
2.2.3 Determination of combined weight
It is given that the combined weight set of indexes is W=[w1,w2,…,wm]T, wi is the weight of No. i index
after combination of the two kinds of weight methods. When wi represents the linear combination of wi'
and wi'' (i=1, 2, …, m), then wi is as follows:
wi = θ wi' + (1 − θ )wi''
(9)
Where, θ refers to the proportion of subjective preference coefficient weight in combined weight; wi'
refers to the weight of No. i index calculated by G1 method; (1-θ) refers to the proportion of objective
preference coefficient in combined weight; wi'' refers to the weight of No. i index calculated by
maximum deviation method .
In order to make the values of multi-attribute comprehensive evaluation for each evaluation object
decentralized as much as possible, the target function J(W) is established with the purpose that the sum
of squares of deviation of the comprehensive evaluation values for No. j evaluation object and all other
evaluation objects is the maximum[21].
n
n
n
m
(10)
J (W ) = ∑ k j (W ) = ∑∑ [ ∑ (bij − bik )wi ]2
j =1
j =1 k =1 i =1
Where, kj(W) refers to the sum of squares of deviation of the comprehensive evaluation values for No. j
evaluation object and other evaluation objects; bij refers to the score of No. i index in No. j evaluation
object (i=1, 2, …, m j=1, 2, …, n).
If the matrix B is as follows:
；
147
n n
∑∑(b1j −b1k)(b1j −b1k)
 j=1k=1
n n
∑∑(b2j −b2k)(b1j −b1k)
B =j=1k=1

M

n n

∑∑(bmj −bmk)(b1j −b1k)
j=1k=1
n n

n n
∑∑(b −b )(b −b ) L ∑∑(b −b )(b −b )
1j
1k
2j
2k
1j
1k
mj
mk


(b2j −b2k)(b2j −b2k) L ∑∑(b2j −b2k)(bmj −bmk)
∑
∑
j=1 k=1
j=1 k=1


M
L
M

n n
n n

b
b
b
b
b
b
b
b
L
(
−
)(
−
)
(
−
)(
−
)
∑
∑ mj mk 2j 2k ∑
∑ mj mk mj mk 
j=1 k=1
j=1 k=1

j=1 k=1
j=1 k=1
n n
n n
(11)
Then Formula (10) can be represented as below:
J(W)=WTBW
(12)
It is given that hypermatrix W * = (W ' , W '' ) , according to the matrix theory, corresponding unit
characteristic vector of the maximum root for symmetric matrix (W*)TBW* is the optimal solution of
Formula (10)[21], and such characteristic vector will become the combined weight coefficient θ after
normalization:
(13)
θ = x11 × x12 /( x11 × x12 + x21 × x22 )
* T
*
Where, xij refers to the element of the matrix (W ) BW in No. i row and No. j column.
The combined weight methods based on the principle of maximum sum of squares of deviation solves
such problems as failure of the subjective weight methods in reflecting the data difference among
various indexes, failure of objective weight methods in reflecting the importance of index attribute in
evaluation and experts’ knowledge and experience.
2.3 Hi-tech zone competitiveness evaluation model based on combined weight process
It is given that Dj is the comprehensive score of No. j evaluation object. According to the comprehensive
evaluation formula of linear weight, Dj shall be as follows:
＝ ∑b w
m
Dj
ij
(14)
i
i =1
Where, bij refers to the score of evaluation index; wi refers to the combined weight of No. i evaluation
index.
3. Positive study
3.1 Evaluation sample and data source
3.1.1 Selection of evaluation sample
17 state-level hi-tech zones in 4 municipalities directly under the Central Government and 13
sub-provincial cities are chosen as evaluation samples. Due to limited space, the full names of hi-tech
zones are replaced by the names of cities, with details as below, including Beijing, Shanghai, Tianjin,
Chongqing, Shenyang, Dalian, Changchun, Harbin, Nanjing, Hangzhou, Xiamen, Jinan, Wuhan,
Guangzhou, Shenzhen, Chengdu and Xi’an.
The reason why the abovementioned samples are typical is that they not only include the four
municipalities directly under the Central Government representing the political and economic centers of
China, but also cover 13 sub-provincial cities representing the political and economic centers in the
provinces in the east, center and west of China, where both coastal open cities and inland cities are
involved.
3.1.2 Source of sample data
After calculation and arrangement, all the data are derived based on the data in 2006 China Torch Plan
Statistic Materials, the Yearbook of China’s Cities 2007, the Statistic Yearbook of China’s Cities 2007,
the website of the Ministry of Science and Technology of China, and the website of the Torch High
Technology Industry Development Center under the Ministry of Science and Technology.
3.2 Scoring of fuzzy membership of indexes
148
The score of fuzzy membership of each index is got after the data of various indexes as shown in Table
1 are input into Formula (1).
3.3 Calculation of combined weight
3.3.1 Determination of weight by G1 method
3.3.1.1 Calculation of the weight of rule layer to objective layer
(1) According to the suggestions from experts, the subjective order of priority for rule layer is as below:
X1 f X2 f X3 f X4
(2) According to the suggestions from experts, the rational value of the importance ratio ri between the
neighboring rule layers is as follows:
r2=X1/X2=1.2,r3=X2/X3=1.3,r4=X3/X4=1.1
(3) When the rational value rk (k=2, 3, 4) of the importance ratio between the neighboring rule layers is
input into Formula (2), the weight of No. 4 rule layer w4=0.1906 can be got.
(4) When w4=0.1906 and r4=1.1 are input into Formula (3), w3=0.2097 can be got. Similarly, we can get
w2=0.2726 and w1=0.3271.
As a result, the subjective weights of rule layer to objective layer calculated by G1 method are as below:
w1=0.3271, w2=0.2726, w3=0.2097 and w4=0.1906.
3.3.1.2 Calculation of the weight of index layer to rule layer
According to the suggestions from experts, the subjective order of priority for the indexes under the four
rule layers are as below: Rule layer X1: X11/X12=1.1; X12/X13=1.3; X13/X14=1.2; X14/X15=1.1; Rule layer X2:
X21/X22=1.1; X22/X23=1.2; X23/X24=1.3; X24/X25=1.1. Rule layer X3: X31/X32=1.3; X32/X33=1.2; X33/X34=1.3;
X34/X35=1.1. Rule layer X4: X41/X42=1.1; X42/X43=1.2; X43/X44=1.1; X44/X45=1.3.
In imitation of calculation of the weight of rule layer to objective layer in Clause 3.3.1.1, the weight of
index layer to rule layer under each rule layer can be got.
3.3.2 Determination of weight by the deviation method
After the scores for 20 indexes in 17 evaluation objects listed in Table 1 are input into Formula (8), the
weight wi'' (i=1,2,…,20) calculated by maximum deviation method is got.
3.3.3 Determination of combined weight
(1) Matrix B is got after the scores for all the indexes of 17 evaluation objects as shown in Table 1 are
input into Formula (11).
(2) The weight calculated by G1 method and the one by deviation method are combined into
hypermatrix:
W * = (W ' , W '' )
(3) Corresponding unit characteristic vector of the maximum characteristic root for symmetric matrix
6.897
(W*)TBW* is 
 6.851
6.851 
.
7.080 
(4) θ =0.496728 is got after the figures in the matrix are input into Formula (13).
(5) Combined weight wi is got after θ, wi' ,and wi'' are input into Formula (9).
(6) The comprehensive score of each evaluation object is got after the scores of evaluation indexes and
wi are input into Formula (14).
4. Analysis on the competitiveness of hi-tech zones at state level (Refer to Tab.2)
4.1 Analysis on technical innovation competitiveness (Rule layer X1)
(1) Hi-tech zones in Shenzhen, Shanghai, Wuhan, etc. boast strong technical innovation competitiveness.
For example, Shenzhen stands fifth in comprehensive ranking, but the top in terms of technical
innovation competitiveness. Shanghai still maintains powerful technical innovation advantages, and
technical innovation has become main force to raise its overall ranking.
(2) Hi-tech zones in Dalian, Xiamen, etc. have relatively weak technical innovation competitiveness.
149
Tab.2 Each layer and comprehensive evaluation scores and orders of competitiveness
for 17 national hi-tech zone
Each rule layer and comprehensive evaluation scores and orders of competitiveness for 17 national hi-tech
(1)
(2)
zone
(3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)
Basis of
Shan
Chon
Chan
Guan
NO
Bei
Tian
Shen Dalia
Harbi Nanji Hang Xiam
Wuha
Shen Cheng
scores
g
g
g
Jinan
g
Xi’an
n
ng zhou en
n
jing
jin
yang n
zhen du
qing
cun
zhou
hai
Rule scores 0.160 0.168 0.084 0.066 0.056 0.052 0.081 0.065 0.121 0.117 0.112 0.110 0.154 0.094 0.179 0.154 0.135
1 layer
2
12
14
16 17
13
15
7
8
9
10
4
11
1
5
6
X1 orders 3
Rule scores 0.160 0.087 0.090 0.120 0.100 0.077 0.053 0.054 0.048 0.129 0.094 0.069 0.118 0.091 0.095 0.162 0.127
2 layer
12
11
5
7
13
16
15
17
3
9
14
6
10
8
1
4
X2 orders 2
Rule scores 0.042 0.152 0.049 0.009 0.064 0.025 0.132 0.023 0.132 0.110 0.225 0.069 0.054 0.064 0.118 0.108 0.045
3 layer
2
12
17
10 15
3
16
4
6
1
8
11
9
5
7
13
X orders 14
3
Rule scores 0.198 0.037 0.019
4 layer
9
14
X4 orders 1
Comp scores 0.560 0.444 0.241
rehens
5
ive
4
13
evalua orders 1
tion
0.016 0.011 0.030 0.025 0.014 0.040 0.048 0.041 0.056 0.054 0.060 0.027 0.032 0.053
15
17
11
13
16
8
6
7
3
4
2
12
10
5
0.211 0.231 0.183 0.291 0.155 0.341 0.406 0.471 0.304 0.380 0.311 0.418 0.455 0.360
15
14
16
12
17
9
6
2
11
7
10
5
3
For example, Xiamen stands second in comprehensive ranking, but ninth in terms of technical
innovation competitiveness. And Dalian stands the last in the latter.
4.2 Analysis on industrial cluster competitiveness (Rule layer X2)
(1) Hi-tech zones in Chengdu, Chongqing, Shenyang, etc. possess strong cluster competitive advantage.
For example, Chengdu stands third in comprehensive ranking, but the top in terms of cluster
competitiveness; Chongqing stands only fifteenth in comprehensive ranking, but fifth in terms of cluster
competitiveness, which has become the major source of its competitiveness.
(2) Hi-tech zones in Shanghai, Nanjing, etc. have relatively weak industrial cluster competitiveness. For
example, Shanghai stands fourth in comprehensive ranking, but twelfth in terms of cluster
competitiveness, which greatly affects the competitiveness ranking of Shanghai; Nanjing stands ninth in
comprehensive ranking, but the last in terms of cluster competitiveness, which has become the
bottleneck for Nanjing to improve its competitiveness.
4.3 Analysis on sustainable development competitiveness (Rule layer X3)
(1) Hi-tech zones in Xiamen, Changchun, Shanghai, etc. show strong competitiveness in sustainable
development. For example, Xiamen stands the top in sustainable development competitiveness ranking.
Changchun stands only twelfth in comprehensive ranking, but is among the top three in terms of
sustainable development competitiveness, showing that Changchun has powerful sustainable
development competitiveness, with better development space.
(2) Hi-tech zones in Beijing, Wuhan, Xi’an, etc. witness relatively weak sustainable development
competitiveness. For example, Beijing stands first in comprehensive ranking, but only fourteenth in
terms of sustainable development competitiveness, which greatly restricts the competitiveness
improvement in Beijing. Xi’an stands eighth in comprehensive ranking, but thirteenth in terms of
sustainable development competitiveness.
4.4 Analysis on pioneering environment competitiveness (Rule layer X4)
(1) Hi-tech zones in Guangzhou, Jinan, Dalian, etc. feature strong pioneering environment
150
8
competitiveness. For example, Guangzhou stands tenth in comprehensive ranking, but second in terms
of pioneering environment competitiveness; Jinan stands eleventh in comprehensive ranking, but is
among the top three in terms of pioneering environment competitiveness, which has played a crucial
role in improving its comprehensive competitiveness.
(2) Hi-tech zones in Shanghai, Chengdu, Shenzhen, etc. see relatively weak pioneering environment
competitiveness. For example, Shanghai stands fourth in comprehensive ranking, but only ninth in terms
of pioneering environment competitiveness, which restricts the comprehensive competitiveness in
Shanghai. Chengdu stands third in comprehensive ranking, but only tenth in terms of pioneering
environment competitiveness, which has become a major bottleneck in improving its competitiveness.
4.5 Analysis on comprehensive competitiveness
Xiamen and Chengdu become new forces suddenly coming to the fore, and respectively stand second
and third in comprehensive competitiveness ranking, showing strong competitiveness and development
potential. Although Beijing still stands first in such ranking, its leading advantages have decreased
gradually. As municipalities directly under the Central Government, Tianjin and Chongqing haven’t seen
strong competitiveness shown in the hi-tech zones thereof, respectively standing thirteenth and fifteenth
in the ranking. The competitiveness in hi-tech zones of the three provinces in the northeast of China are
relatively weak, and Changchun, Shenyang, Dalian and Harbin respectively stand twelfth, fourteenth,
sixteenth and seventeenth in the ranking.
5. Conclusion
(1) The traditional setup of hi-tech zones at state level is changing on the quiet. As shown in positive
study, hi-tech zones in Chengdu, Xi’an, Hangzhou, etc. have improved their competitiveness by cluster
development, and their comprehensive competitiveness comes out in front, reflecting that industrial
cluster competitiveness has become the key to comprehensive competitiveness improvement.
(2) Hi-tech zones in Shenzhen, Shanghai, Wuhan, etc. possess strong technical innovation ability, and
technical innovation has become their powerful competitive advantage. Zhongguancun, Beijing, known
as Chinese Silicon Valley, has witnessed its leading advantage in technical innovation competitiveness
disappear gradually.
(3) Hi-tech zones in Xiamen, Shanghai, Changchun, etc. boast strong sustainable development
competitiveness, with better development potential.
(4) Hi-tech zones in Beijing, Guangzhou, Jinan, etc. enjoy relatively favorable pioneering environment.
And pioneering environment competitiveness has become one major force to improve their
comprehensive ranking.
(5) According to the principle that the sum of squares of deviation of evaluation results for each
evaluation object is maximum, the subjective weight coefficient and objective weight coefficient are
respectively determined as 0.497 and 0.503, which not only reflects the logic thinking that during the
evaluation process, the weight value will be larger if the difference of indexes is greater, but also reveals
the influence of actual conditions on weight evaluation as well as dynamic changes of indexes in future.
References
[1]. Porter M E. The Competitive Advantage of Nations[M].New York: Free Press,1990:33-271
[2]. Porter M E. Clusters and the New Economics of Competition[J].Harvard Business
Review,1998,76(6):77-90
[3]. Pyke F, Sengenberge W. Industrial Districts and Local Economic Regeneration[M].Geneva:
International Institate for Labour Studies,1992
[4]. Rosenfeld Stuart A. Bringing Business Clusters into the Mainstream of Economic Development[J].
European Planning Studies,1997,5(1):3-23
[5]. Zhe Chun-kui. The Summarize of Competitiveness Theory Abroad[J].Productivity
Research,2004,(1):187-188(in Chinese)
151
[6]. Michael E.Porter. Competitive Advantage[M]. Beijing: Hua Xia Press,1997,3-10
[7]. Xie Zhang-Shu, Zhu Bin. Hi-Tech Industry Competitiveness Evaluation Indices
Establishment[J].Science Research Management,2001,(3):1-6(in Chinese)
[8]. Everett M. Rogers, Judith K. Larsen.Silicon Valley Fever[M].Economic Science Press,1985,
62-130
[9]. Gu Chao-lin. The High-Tech Industries and The Park of China[M].China Citic
Press,1998,194-205(in Chinese)
[10]. Saxenian A. Regional Networks and the Resurgence of Silicon Valley[J].California Management
Review,1990, 33:89-112
[11]. YI Jun; TANG Zi. An Appraisement of the Developing State of High-tech Industrial Development
Zones in Northeast China[J].Science of Science and Management of S.& T,2005,26(3):94-96(in
Chinese)
[12]. Li lin,Chen Xiao-hong. The Research of Hi-Tech Zone Competitiveness Evaluation Index System
on Industrial Cluster[J].Social Scientist,2005,(3):57-61(in Chinese)
[13]. Shao Xue-qing, Lu Bo-wen. Research on The Appraisement of The National High-Tech
Parks[J].Studies in Science of Science,2007,25(4):671-675(in Chinese)
[14]. Xu Chen-sheng. Evaluation of The Relative Efficiency of High-Tech Zones Based on
DEA[J].Science & Technology Progress and Policy,2007,24(8):117-119(in Chinese)
[15]. Zhang Wei,Gu Chaolin, Qiu Youliang, Chen Tian. A STUDY ON COPREHENSIVE
EVALUATION OF HIGH/NEW TECHNOLOGY DEVELOPMENT ZONES IN
CHINA[J].GEOGRAPHICAL RESEARCH,1998, 17(3):25-28(in Chinese)
[16]. Liu Kang-hao, Liu Zhi-guo. Research on evaluation for the competition ability of areal investment
environment
based
on
fuzzy
theory[J].Technological
Development
of
Enterprise,2006,25(9):50-52(in Chinese)
[17]. Zhu Shao-peng, Zeng Gang. The Analysis of Competitiveness Indicators of High-Tech Zones
Based on AHP Method-Example as Zhongguancun, Zhangjiang, Shenzhen[J].Industrial
Technology & Economy,2008,27(6):101-106(in Chinese)
[18]. Yu Pengfei,Wang Lina.The choice of sample data dimension less methods in Financial Early
Warning[J].Friends of Accounting,2005,(12):43-44(in Chinese)
[19]. Guo Yajun.The Theory and Methods of Comprehensive Evaluation[M].Beijing, Science
Press,2002:7-12(in Chinese)
[20]. XIAO Xin-ping,LI Fu-qin,TU Jin-zhong.Application of Grey Relational Analysis Based on
Maximizing
Deviations
to
Comprehensive
Evaluation
for
Highway
Network[J].Highway,2006,(8):122-123(in Chinese)
[21]. CHEN Wei, XIA Jianhua. An Optimal Weights Combination Method Considering both Subjective
and Objective Weight Information[J]. Mathematics in Practice and Theory,2007,37(1):17-22(in
Chinese)
152