Evaluating innovation capabilities for science parks: a system model/Mokslo ir technologiju parku inovacinio pajegumo ivertinimas: sistemos modelis.
Zeng, Saixing ; Xie, Xuemei ; Tam, Chiming 等
1. Introduction
Science parks were established to stimulate the formation and
development of new technology-based firms (Siegel et al. 2003; Sun and
Lin 2009). When combined effectively with various institutions (e.g.
government, research institutions and universities), science parks have
played an important role in promoting innovation, entrepreneurship,
growth of knowledge-based companies and in turn economic growth within
their regions (Adekola et al. 2008; Lindelof and Lofsten 2003). Such
science parks were first originated in the western world; especially,
the remarkable development of Silicon Valley and Route 128 in the U.S.
Observers have noted that science parks help create an innovative
environment which can breed a continuous stream of innovations in an
environment of information sharing and knowledge spillover (Yam et al.
2004), both across and between firms and academic institutions, via
informal channels (Saxenian 1996). Because of the technology
integration, high value addition and valid spillover mechanism of
knowledge and technique, science parks have strong creative advantages
(Hu 2007). Hence, both developed and under-developed countries have
tried to mimic the American success stories by encouraging formation of
science parks. Better-known examples of such parks include Cambridge in
the U.K., Sophia-Antipolis of France, Tsukuba in Japan, and
Taiwan's Tsinchu Science Park (Vaidyanathan 2008).
Although science parks in China have been growing rapidly in the
past decade, a few successful examples have been noted (Hu 2007).
However, a series of issues need to be faced, such as irrational
allocation of resources, incomplete innovation network, the lack of
innovation environment and so forth, which affect continuous improvement
of innovation capabilities for science parks (Liu and White 2001; Tan
2006). There is, to our knowledge, a paucity of studies on evaluating
innovation capabilities of science parks in China. This paper aims to
develop a model to measure innovation capabilities of science parks. A
case study is conducted based on the Qingdao Science Park, one of the
national science parks. It is hoped to provide a better understanding on
how to improve innovation capabilities of science parks in China.
2. Previous works
The success of science parks in promoting technology transfer and
attracting clusters of highly innovative firms has motivated countries
from around the world in an attempt to promote regional development (Tan
2006). Harper and Georghiou (2005) described the process and outcomes of
an exercise that used the 'success scenario' methodology to
develop a shared vision of the future of business-university linkages in
the city region of Manchester. They presented a scenario in five
dimensions: infrastructure, human resources, university missions, inward
investment, and networking.
Colombo and Delmastro (2002) compared a sample composed of 45
Italian new technology-based firms (NTBFs) which at the beginning of
2000 were located on a technology incubator within a science park with a
control sample of off-incubator firms. Aspects considered include the
personal characteristics of founders of NTBFs, the motivations of the
self-employment choice, the growth and innovative performances of firms,
propensity towards networking, and access to public subsidies. They
found that Italian parks managed to attract entrepreneurs with better
human capital, as measured by educational attainments and prior working
experience. In addition, on-incubator firms show higher growth rates
than their off-incubator counterparts. They also perform better in terms
of adoption of advanced technologies, attitude to participating in
international R&D programs, and establishment of collaborative
arrangements, especially with universities.
Bakouros et al. (2002) compared the three science parks of Greece
and found that they were not the same in terms of the links between
universities and industries. Informal links have been developed between
firms and local universities; however, only the firms located at one
science park have developed formal links, while the formal links of the
companies of the other two parks are at the infancy level by then.
Synergies between on-park companies are limited only to commercial
transactions and social interactions (Sofouli and Vonortas 2007). The
research-type synergies are completely absent in all the three parks. By
investigating a science park in Hungary, the first institute of the kind
in Central Eastern Europe, Palmai (2004) found some signs that indicated
saturation of the company's virtual incubation activity.
As Vaidyanathan (2008) indicated, the government of India
established the software science parks of India (STPI) scheme and opened
numerous software parks around the country. These parks have played a
critical role in the growth of India's software sector. In recent
years, private software parks have also been established in different
parts of India. The government of India is now promoting biotechnology
(biotech) parks to encourage growth of this emerging sector.
Biotech-Information Technology (Bio-IT) park is the next type of park
that the government is planning to promote.
Lai and Shyu (2005) explored the innovation capacity in two
different science parks across the Taiwan Strait. They chose the
Zhangjiang High-Tech Park (ZJHP) of China and the Hsinchu Science-based
Industrial Park (HSIP) of Taiwan to compare their innovation capacity.
They developed a model to analyze the science parks in innovation
capacity across the Taiwan Strait and found differences in determinants
for innovation capacity between the ZJHP and HISP, such as the
"basic research infrastructure", "sophisticated and
demanding local customer base", and "the presence of clusters
instead of isolated industries"
Tan (2006) investigated a specific example of an industry cluster
in China, the Beijing Zhongguancun (ZGC) Science Park, which has
accommodated the largest cluster of semiconductor, computer, and
telecommunication firms in China, consisting of both domestic and
foreign invested firms. Tan (2006) examined the origin and growth of
industry cluster in a traditionally heavily regulated economy and
region, its role in promoting technology transfer and innovation, and
challenges that firms will face in the future.
Chan and Lau (2005) provided an assessment framework of technology
incubators in the science park, including advantages from pooling
resources, sharing resources, consulting services, positive effect from
higher public image, networking advantages, clustering effect,
geographic proximity, cost subsidies and funding support. Using business
development data of six technology start-ups in the Hong Kong Science
Park, the framework is then applied to examine the effectiveness of
incubators from the perspective of venture creation and development
process. They found that the benefits required by technology founders at
different stages of development are varied and therefore, the general
merits that are claimed by incubators as useful to technology start-ups
are debatable.
3. Research methodology
3.1. System for evaluating innovation capabilities for science
parks
Based on the literature review (Chan and Lau 2005; Palyvoda 2008;
Zeng et al. 2010), this paper has developed a system for evaluating the
innovation capabilities of science parks in China. The system is
composed of the Innovation Organization Sub-System (IOSS, mainly
high-tech firms), the Innovation Support Sub-System (ISSS, e.g.,
technology intermediaries) and the Innovation Environmental Sub-Systems
(IESS), as shown in Fig. 1.
Various elements of sub-system in the synergic system are
summarized in Table 1.
[FIGURE 1 OMITTED]
From Table 1, it shows that IOSS includes the innovative firms and
research institutions. Innovative firms are the most important part of
science parks. Research institutions are the main source of knowledge
spill and technology sharing (Mu and Lee 2005). ISSS, consisting of
facilities for innovation and technology intermediaries, could provide
correlative services for IOSS. IESS, which can provide clusters with a
suitable environment and a system protection, is an indispensable part
for continuous innovation of science parks.
3.2. Evaluation indicators
In this paper, evaluation indicators, proposed by the science and
technology development strategy of the Chinese research group (2005),
are used for assessing innovation capability for science parks,
including IOSS, ISSS and IESS, as listed in Table 2.
3.2.1. Innovation Organization Sub-System
The indicators of IOSS, including the innovation capability of
inputs, outputs and growth, are used to evaluate the innovative
sustainability of firms, universities and research institutions. The
innovative input capability mainly involves the input for R&D
funding and R&D staff (Zhou and Leydesdorff 2006; Zeng et al. 2009).
The extent of input determines the intensity of innovative activities,
the effectiveness of innovative output and the sustainability of
firms' innovation (Motohashi and Xiao 2007). The innovative output
capability includes the regional GDP per capita and the total income
from technology, industry and trade. The output performance of
innovation focuses on whether it could create more wealth and facilitate
growth of regional GDP and its contributions to the regional economic
development. The innovative growth capability, involving the annual
growth rate of R&D staff (Squicciarini 2008) and expenses, is the
combination of innovation in the latitude of time and space and reflects
the dynamic characteristics of innovation in clusters. In short, the
IOSS is the core of the continuous operation for cluster innovation and
the main source of continuous innovative capability.
3.2.2. Innovation Support Sub-System
The indicators of ISSS include innovation infrastructure and
technology intermediary. Innovation infrastructure involves information
facilities of science parks. Obviously perfect infrastructure will be in
favor of the continuous innovation of clusters. The technology
intermediary consisting of technology transfer centers and incubators is
regarded as the bridge for knowledge spill and technology diffusion
(Sofouli and Vonortas 2007).
3.2.3. Innovation Environmental Sub-Systems
The indicators of IESS include policies and regulations, cluster
and financial environments. Policy environment indicators examine the
supportive intensity of the government in the development of clusters
and the degree of protection to intellectual property rights in
clusters. Cluster environment indicators, which can provide an impetus
for the development of firms in clusters, explore the extent of
collaboration of universities, industries, the government and the degree
of industrial correlativity in clusters. Financial environment
indicators, including government investment and funds for incubators,
are to evaluate the degree of funding support from financial
institutions or local governments.
4. Model development
4.1. The model
Factor analysis is a statistical analysis method for managing
problems with multiple-variable data. Its basic principle is to trim
down a large number of initial variables into a linear combination of a
few factors, which are used to reveal and explain a complex
socio-economic phenomenon. Therefore, factor analysis is used widely to
establish a simple structural model to reveal the essential relationship
among complicated data sets.
The model of factor analysis can be represented by Equation (1):
X = AF + [epsilon]. (1)
Specifically, the model of factor analysis assumes that each random
variable [X.sub.i] that can be observed depends on a small number of
random variables [F.sub.1], [F.sub.2], ... [F.sub.m] (Common Factor) of
non-observation and Unique Factor [[epsilon].sub.i]. Therefore, Equation
(1) can be transformed into Equation (2):
[X.sub.i] = [a.sub.i1] [F.sub.1] + [a.sub.i2] [F.sub.2] + ... +
[a.sub.im] [F.sub.m] + [[epsilon].sub.i], (2)
where [a.sub.ij], known as Factor loading, denotes the load of
variable i acting on factor j. Then, these random variables are assumed
in Equations (3)-(7):
E([F.sub.j]) = 0, (3)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1), (4)
E([[epsilon].sub.j]) = 0, (5)
Cov([[epsilon].sub.i], [[epsilon].sub.j]) = diag
([[sigma].sup.2.sub.1], [[sigma].sup.2.sub.2], ...,
[[sigma].sup.2.sub.p] = D (2), (6)
Cov([F.sub.i], [[epsilon].sub.j]) = 0, (7)
Based on these assumptions, the model exhibits the following
characteristics:
1. The mean of each Common Factor is 0 and the variance is 1.
Furthermore, there is no correlation among all the Common Factors.
2. The mean of Unique Factor is 0, with unequal variances and no
correlation between them.
3. There is no correlation between Common Factors and Unique
Factors.
4.2. Case study
Since early 1990s, the Chinese government has established science
parks in 53 major cities under its 'Torch' Program, a science
and technology initiative to promote technology transfer and diffusion
(Guan and Ma 2007; Hu 2007). In this study, the Science Park at Qingdao,
one of the national science parks, is chosen for investigation. The data
(3) from 1994 to 2008 are collected. Based on the formation and growth
of the Science Park at Qingdao, 22 indicators are employed as the
original variables to gauge the innovation capability of the Science
Park.
To compare data of different dimensions, the improved Efficacy
Factor Method is adopted to standardize and normalize the original data.
The formula is shown in Equation (8).
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (4) (8)
In Equation (8), [d.sub.i] represents the relative value of the
indicator i after standardization and normalization. And the range of
[d.sub.i] is from 60 to 100. [X.sub.i] is the true value of the
indicator i. The variable [X.sub.max] denotes the maximum value of the
time series and [X.sub.min] denotes the minimum value of the time
series. [X.sub.i] - [X.sub.min] means the difference between the true
value and the minimum value for indicator i when it is a positive
indicator, and [X.sub.i] - [X.sub.max] means the difference between the
true value and the maximum value for indicator i when it is a negative
indicator.
In our study, [X.sub.max] is the maximum value for the indicator i
during the period from 1994 to 2008, and [X.sub.min] is the minimum
value for the indicator i during the period from 1994 to 2008. As all 22
indicators in this study are positive, [X.sub.i] - [X.sub.min] is the
difference between the true value and the minimum value for indicator i
during the period from 1994 to 2008, and [X.sub.max] - [X.sub.min] is
the difference between the maximum value and the minimum value for
indicator i during the period from 1994 to 2008.
5. Results and analysis
In this paper, the statistical software, SPSS 16.0, is used to
analyze the 22 original indicators. As the number of indicators is
larger than the number of samples, the KMO and Bartlett's Test of
Spherical cannot be exercised. Consequently, the suitability of
analyzing by Factor Analysis is based on the results of Correlation
Matrix and Anti-Image Correlation Matrix (Hair et al. 1995). From the
results of data analysis, the P-value of correlation coefficient matrix
is less than 0.05 and the correlation coefficient between variables is
larger than 0.3 (when most of the correlation coefficients are less than
0.3 in the correlation matrix, it is not suitable to use Factor
Analysis) (Mulaik 1990). Moreover, most values of Anti-Image Correlation
Matrix are small. Accordingly, there are significant correlations
between indicators so that it is appropriate to adopt Factor Analysis.
In addition, the communalities test of indicators shows that most of the
communalities are larger than 0.85, which indicates that the common
factors have a strong explanatory power and thus Factor Analysis is
effective.
In Step 2, in light of the standardized correlation coefficient
matrix, factor eigenvalues and cumulative variance of innovation
capability of science parks are obtained (see Table 3).
'Principal Component Analysis' is applied to extract
irrelevant linear combination of variables. From Table 3, it shows that
three main eigenvalues of correlation coefficient matrix are extracted.
The first component has the maximum variance of 15.638, accounting for
71.083% of the total variance and the cumulative percentage of standard
deviation of three components together achieves at 88.262% (the total
number of factors extracted is determined by the cumulative variances
with contributions more than 85%). That reveals that the information
described by the 22 initial variables can be reflected by these three
components. Therefore, the method of Principal Component Analysis is
applied to extract the first three factors as the integrated component.
The findings show that it is satisfactory that the first three factors
can describe most of the information of the initial variables.
To illustrate the significance of factors more clearly and analyze
the actual problem more pertinently, factor loadings are rotated to make
the typical variables of each component more prominent. Furthermore, the
method of Varimax is adopted, which is an orthogonal rotation method,
making each factor bearing the least variance while having the maximum
load. The rotated component matrix is shown in Table 4.
From Table 4, it shows that the component [F.sub.1] mainly explains
the variables of [X.sub.1], [X.sub.3], [X.sub.4], [X.sub.5], [X.sub.12],
[X.sub.14], [X.sub.15], [X.sub.16], [X.sub.17], [X.sub.18], [X.sub.19],
[X.sub.20], in which the variables of [X.sub.1], [X.sub.3], [X.sub.4],
[X.sub.5] denote indicators of input and output, and the variables of
[X.sub.12], [X.sub.14], [X.sub.15], [X.sub.16], [X.sub.17], [X.sub.18],
[X.sub.19], [X.sub.20] represent indicators of growth of continuous
innovation for science parks. Thus, the component [F.sub.1] reflects the
level of continuous innovation for science parks.
Also, the component, [F.sub.2], can embrace the variables of
[X.sub.8], [X.sub.9], [X.sub.11] which are the main estimative
indicators for innovation efficiency. Consequently, the component
[F.sub.2] reflects the efficiency of continuous innovation for science
parks.
The variables of [X.sub.5], [X.sub.6] and [X.sub.7] as the
evaluated indicators for measuring the effect of sustainable innovation
are included in [F.sub.3]. Thus, [F.sub.3] reflects the effect or impact
of continuous innovation for science parks. Specific naming of
components is summarized in Table 5.
In order to investigate the significance of variables to components
and start the comprehensive evaluation, the following steps are adopted
to calculate the component scores. To minimize the error, regression is
used to estimate components and obtain the Component Score Coefficient
Matrix with the results tabulated in Table 6. Thus, according to the
coefficient matrix and observed values of variables, component scores
are calculated.
The various individual component scores are calculated from
Equation (9):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (9)
Note that [b.sub.j] stands for the contribution rate of variance
for each component, and
[b.sub.j] = [[lambda].sub.j] / P; (10)
where [[lambda].sub.j] is the eigenvalues of J in the initial
correlation matrix and P = [[lambda].sub.1] + [[lambda].sub.2] + ... +
[[lambda].sub.m];
Then, the composite evaluation score is expressed in Equation (11):
F = [m.summation over (j=1)] [b.sub.j][F.sub.i], i = 1, 2, ..., n.
(11)
In this paper, F = [b.sub.1] x [F.sub.1] + [b.sub.2] x [F.sub.2] +
[b.sub.3] x [F.sub.3;] i = 1,2,3; j = 1,2,3; viz.m = 3;
[b.sub.1] = 15.638/19.418 = 0.805, [b.sub.2] = 2.186/19.418 =
0.113, [b.sub.3] = 1.594/19.418 = 0.082, F = 0.805 x [F.sub.1] + 0.113 x
[F.sub.2] + 0.082 x [F.sub.3]. (12)
Hereby, we obtain the individual component scores and the composite
component scores for the period of 1994-2008 for the Science Park at the
Qingdao city in China. The results are shown in Table 7.
From Table 7, the degree of continuous innovation at the Qingdao
Science Park can be abstracted. The composite capability of continuous
innovation shown in Table 7 is derived according to the actual
development trends and the internal growth process of Science Park based
on a holistic view. The score of capability of continuous innovation for
the Qingdao Science Park is the highest in 2008 when compared with those
of previous years. Although, it underwent a decline from the year 2003
to 2004, and 2007, the composite component score (Viz. F) has, as a
whole, been increasing annually (see Fig. 2).
Then, trends of scores of the three individual components are shown
in Table 7 and Fig. 3.
[FIGURE 2 OMITTED]
[FIGURE 3 OMITTED]
From Fig. 3, the component score of [F.sub.1] (viz. level component
of continuous innovation for science parks) has almost increased
steadily (especially from a negative value into a positive value
gradually) for the years of 1994 to 2008, which implies that the level
of continuous innovation of the Qingdao Science Park in China has been
improved gradually. The main reason is that the various parties have
increased the inputs of innovation elements (especially the input of
R&D expenses and R&D staff), which can upgrade the capacities of
input and output of continuous innovation.
The trend of component scores for [F.sub.2] (viz. efficiency
component of continuous innovation for science parks) shows some
irregularity and the score shows a rapid downward trend for the years of
1997 to 2002, and reached the lowest point in 2002, resulted from the
decrease of input of R&D funds and staff. All these factors have led
to inefficient innovation effect, low growth rate of technology firms
and slow pace of development. In 2007, the score of [F.sub.2] reached
the maximum of 1.777, which mostly related to the increase in investment
of R&D and the increase of high-tech firms. While the score of
[F.sub.2] decrease in 2008 and the possible reason lies in the impact of
the world financial crisis.
Irregularity is shown in the trend of the component scores for F3
(viz. effect component of continuous innovation for science parks),
which implies the deficient input and insufficient government regulation
resulting in decrease in patents, foreign exchange, exports and
industrial output. In 2008, the score of [F.sub.3] reached the maximum
of 2.418 during the years of 1994-2008, which mostly related to the
increase in funds and investment of science and technology (e.g. the
increases of R&D personnel and the increases of profits). While the
score of [F.sub.3] bottoms out in 2004 and the fundamental reason lies
in the weak sustainable growth capability. Moreover, temporary increase
in investment is unable to strengthen the innovation capability
continuously.
Based on the above analysis, it indicates that the three components
([F.sub.1], [F.sub.2] and [F.sub.3]) abstracted via the method of Factor
Analysis can adequately explain the evaluation indicators of continuous
innovation for science parks. On the other hand, the evolution law
explained by the evaluation system fitted by three components is
consistent with the actual evolution process of the Qingdao Science
Park. Therefore, the empirical results confirm that this evaluation
system bears a good explanatory power.
6. Concluding remarks
"Science Park", an important means of forming a regional
cluster leading to sustainable regional developments, has made
significant achievements recently in China. However, the capability to
continuous innovation of science parks remains weak. Based on the
empirical results of the previous analysis, the main reasons are as
follows:
Firstly, there is a lack of effective innovative culture between
universities and research institutions (including public and private
ones). A proper innovative culture can accelerate the flow rate of
innovation production, spillover and diffusion for universities and
research institutions that can determine the sustainability of
innovation. In this aspect, the science parks in China are confronted
with serious problems of lack of cooperation. Even more seriously, the
proportion of talent is low and there is a lack of cooperation between
high-tech firms, universities and research institutions, so that an
effectively interactive mechanism has not been built up in the science
parks of China.
Secondly, environmental concerns for continuous innovation are
inadequate. Most science parks in China are formed under the privilege
of preferential policies, good transportation infrastructure and the
abundant supply of low-cost labor, which can attract both domestic and
foreign firms. These science parks can hardly enjoy a genuine
"synergy effect" and "innovation culture" and the
cooperative relationship between the upstream industries and downstream
industries is not yet well established. It results in fewer intercourse
and collaboration within and across the innovative parts, among firms,
government agencies, universities and research institutions.
Therefore, it is crucial for science parks to enhance the linkages
and cooperation among universities, research institutions and firms so
as to promote a sustainable development and continuous innovation in the
regional economy.
To overcome the problems, the intercourse and sharing of technical
know-how between firms, research institutions and universities should be
strengthened in order to improve innovation capability. In this regard,
the role of universities for the regional economic development needs to
be given full play. Given the lack of resources and experienced staff in
technology transfer offices of some universities, current activities in
technology transfer should be supported and strengthened. Also, there
needs to be some channels for high-tech firms to access university
technologies. Furthermore, university technology could be marketed as an
"outsourcing route" for R&D activities of some firms.
In addition, the innovative infrastructure in science parks should
be further strengthened, which include optimizing the regional
environment, making full use of these infrastructure and public building
products, increasing government funding to the university-industry
interface to provide higher level of transparency, encouraging
communication, establishing an efficient information network to
accelerate flow of information, strengthening construction of technology
intermediaries, and improving the professional quality of employees.
Technology intermediaries, a bridge to connect the firms with research
institutions, play an important role at the process of the regional
sustainable development. What more important for science parks are to
perfect the technology intermediaries and establish an effective
mechanism to capitalize on the synergy effect of research institutions,
universities and firms by turning them into a "commonwealth of the
intermediary market". Meanwhile, the government policies on
technology innovation are indispensable, which can target large,
multi-disciplinary and long-term programs by emphasizing the
"selection and concentration" strategy for the efficient use
of R&D resources.
Finally, the key leading to success of science parks is to
establish a favorable culture for regional sustainable innovation. To
form a good environment, science parks may need to foster a culture that
encourages innovation, entrepreneurship and a proper environment
conducive to the free flow of innovation ideas and know-how (e.g.
talents, technology, information and knowledge, etc.).
doi: 10.3846/tede.2010.25
Acknowledgements
The authors gratefully acknowledge the very helpful comments and
suggestions given by two anonymous referees. This study is supported by
the National Natural Science Foundation of China (Grant no. 70772067),
the Ministry of Education of China (Grant no. 20090073110029, Grant no.
NCET-06-410), and the Shuguang Planning of Shanghai Education
Development Foundation (Grant no. 06SG17).
Received 12 September 2009; accepted 5 August 2010
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(1) Equation (4) indicates that the variances of all Common Factors
are 1, and there is no correlation among all the Common Factors.
(2) Equation (6) indicates that the variance of Unique Factor is
[[sigma].sup.2.sub.i], and there is no correlation among all the Unique
Factors.
(3) Data source: China Statistics Yearbook on Science Parks
(1994-2008). The surveyed data is collected from Science Park of Qingdao
city in China.
(4) When i is a positive indicator, it means the bigger the true
value for i, the better of the evaluation result will be. While when i
is a negative indicator, it means the lesser the true value for i, the
better of the evaluation result will be. In this paper, all 22
indicators are positive.
Saixing ZENG. Doctor, Head and Professor in Antai School of
Management at Shanghai Jiaotong University, China. As a researcher in
technology management and related fields, he has managed a large number
of research projects, and has published more than 120 journal and
conference papers, books, and reports on technology management and
project management. Research interests: Technology management, Project
management.
Xuemei XIE. Doctor, Lecturer in School of Management at Shanghai
University, China.Dr. Xie completed her Ph. D study in 2009. She worked
as Research Associate at City University of Hong Kong in 2007 and in
2009. Dr. Xie has published 20 scientific articles. Research interest:
Technology management.
Chiming TAM. Doctor, Professor in College of Science &
Engineering at City University of Hong Kong, Hong Kong. Professor Tam
obtained a PhD in Loughborough University, UK in 1993. He has been as
leaders of several teaching programs and successfully supervised a
number of PhD and MSc students. Professor Tam has published more than
150 international refereed journal papers. Reserach interest: Project
management.
Saixing Zeng [1], Xuemei Xie [2], Chiming Tam [3]
[1] Antai School of Management, Shanghai Jiaotong University,
Shanghai 200052, P.R. China
[2] School of Management, Shanghai University, Shanghai 200444,
P.R. China
[3] College of Science and Engineering, City University of Hong
Kong, Kowloon, Hong Kong
E-mail: [1] zengsaixing@sjtu.edu.cn; [2] xxm1030@126.com; [3]
bctam@cityu.edu.hk
Table 1. Elements of Innovation System for Science Parks
Sub-System Element Sub-Element
Innovative Manufacturers (high-tech firms),
Innovation Firms Related enterprises (e.g.
Organization suppliers, vendors, distributors
Sub-System and customers), Competitive
(IOSS) enterprises, etc.
Research Knowledge production institutions
Institutions (e.g. universities and research
institutions), Technical production
institutions (R&D departments of
large enterprises, the laboratories
in the university, etc.)
Innovation Innovation Hardware infrastructure
Support Sub- Infrastructure (transportation, communications and
System (ISSS) utilities, etc.), Soft elements of
the environment for cluster
innovation (technology, cultural
facilities, information services,
training services, management
systems, etc.).
Technology Information technology
Intermediaries intermediaries (e.g. non-profit
making government agencies,
business information intermediary),
Technology agents, Innovation
incubators, Regional technology
centers, etc.
Innovation Policies and Industry policies, Technology
Environmental Regulations policies, Tax incentives, Policies
Sub-Systems on SMEs incubators and other
(IESS) related policies.
Cultural Local social and cultural
Environment environment (social customs,
values, etc.), Cluster culture of
innovation within the network (e.g.
informal organizations, tacit
knowledge), Innovative culture
system of hightech enterprise
(sense of innovation, teamwork,
entrepreneurship, etc.)
Financial Financial institutions, Credit
Environment system, Risk investment, Capital
market system, etc.
Table 2. Indicators for evaluating Innovation Capability for Science
Parks
Sub-System Indicator
IOSS Ratio of R&D funding to the regional GDP ([X.sub.1])
Newly granted patents per millions of people ([X.sub.2])
Ratio of R&D spending to the regional total technology
spending ([X.sub.3])
Ratio of R&D staff to employees ([X.sub.4])
Regional GDP per capita ([X.sub.5])
Ratio of exports to the total income from technology,
industry and trade ([X.sub.6])
Gross industrial output value ([X.sub.7])
Annual growth rate of R&D staff ([X.sub.8])
Annual growth rate of R&D expenses ([X.sub.9])
Ratio of annual growth rate of profits to the total
income from technology, industry and trade ([X.sub.10])
ISSS Annual growth rate of high-tech enterprises ([X.sub.11])
The ratio of internet users ([X.sub.12])
Turnover of technology market ([X.sub.13])
The number of technology intermediaries ([X.sub.14])
The number of practitioners in technology
intermediaries ([X.sub.15])
The annual number of incubators graduated ([X.sub.16])
IESS * The degree of being protected for Intellectual
Property in cluster ([X.sub.17])
* The satisfactory degree to clusters policy ([X.sub.18])
* The degree of industry correlation ([X.sub.19])
* The degree of cluster cooperation ([X.sub.20])
Annual growth rate of regional investment ([X.sub.21])
The total fund for incubators ([X.sub.22])
Note: * The indicators are measured by a qualitative index, which
needs to be quantified.
Table 3. Total Variance explained
Initial Eigenvalues
% of Cumulative
Component Total Variance %
1 15.638 71.083 71.083
2 2.186 9.936 81.018
3 1.594 7.244 88.262
Rotation Sums of Squared Loadings
% of Cumulative
Component Total Variance %
1 12.307 55.939 55.939
2 4.890 22.229 78.169
3 2.221 10.094 88.262
Note: Extraction Method- Principal Component Analysis.
Table 4. Rotated Component Matrix (a)
Component
Variable 1 2 3
[X.sub.4] 0.952 0.084 0.050
[X.sub.20] 0.952 0.150 0.015
[X.sub.19] 0.943 0.214 -0.095
[X.sub.17] 0.930 0.277 0.112
[X.sub.1] 0.926 0.150 -0.060
[X.sub.18] 0.912 0.222 -0.290
[X.sub.5] 0.863 0.487 0.008
[X.sub.3] 0.856 0.471 0.029
[X.sub.15] 0.852 0.508 0.048
[X.sub.21] 0.843 0.214 0.418
[X.sub.12] 0.821 0.175 0.329
[X.sub.16] 0.785 0.593 0.135
[X.sub.22] 0.761 0.636 -0.038
[X.sub.14] 0.759 0.556 0.283
[X.sub.2] 0.515 0.743 0.112
[X.sub.13] 0.640 0.724 0.178
[X.sub.10] 0.672 0.693 0.171
[X.sub.6] 0.034 -0.675 -0.015
[X.sub.7] 0.665 0.670 0.284
[X.sub.11] -0.281 -0.090 -0.811
[X.sub.9] 0.123 -0.374 -0.762
[X.sub.8] -0.172 -0.444 0.574
Notes: Extraction Method-Principal Component Analysis; Rotate
Method-Varimax with Kaiser Normalization; (a) Rotation converged in
six iterations.
Table 5. Naming of Component
Component Component
[F.sub.1] [F.sub.2]
Variable [X.sub.1], [X.sub.3], [X.sub.8], [X.sub.9],
[X.sub.4], [X.sub.5], [X.sub.11]
[X.sub.12], [X.sub.14],
[X.sub.15], [X.sub.16],
[X.sub.17], [X.sub.18],
[X.sub.19], [X.sub.20],
[X.sub.21], [X.sub.22]
Naming of Level component of Efficiency component of
component innovation capabilities innovation capabilities
for science parks for science parks
Component F3
[F.sub.3]
Variable [X.sub.2], [X.sub.6],
[X.sub.7], [X.sub.10],
[X.sub.13]
Naming of Effect component of
component innovation capabilities
for science parks
Table 6. Coefficient Matrix of Component Scores
Variable Component
1 2 3
[X.sub.1] 0.137 -0.121 -0.051
[X.sub.2] 0.109 -0.121 0.140
[X.sub.3] 0.055 0.038 -0.035
[X.sub.4] 0.155 -0.167 0.009
[X.sub.5] 0.053 0.046 -0.047
[X.sub.6] 0.109 -0.135 -0.345
[X.sub.7] -0.024 0.150 0.075
[X.sub.8] 0.058 -0.224 0.325
[X.sub.9] 0.150 -0.325 0.052
[X.sub.10] -0.025 0.089 -0.391
[X.sub.11] -0.065 0.232 -0.015
[X.sub.12] -0.027 0.170 0.017
[X.sub.13] -0.039 0.190 0.018
[X.sub.14] 0.017 0.077 0.084
[X.sub.15] 0.047 0.055 -0.029
[X.sub.16] 0.015 0.102 0.007
[X.sub.17] 0.109 -0.076 0.022
[X.sub.18] 0.121 -0.063 -0.171
[X.sub.19] 0.127 -0.091 -0.074
[X.sub.20] 0.141 -0.133 -0.014
[X.sub.21] 0.104 -0.115 0.180
[X.sub.22] 0.003 0.143 -0.082
Notes: Extraction Method-Principal Component Analysis. Rotate
Method-Varimax with Kaiser Normalization.
Table 7. Component Scores of Innovation Capability of Qingdao Science
Park
Year [F.sub.1] Ranking [F.sub.2] Ranking
1994 -1.743 15 0.261 7
1995 -1.443 13 0.263 6
1996 -1.447 14 0.537 4
1997 -0.898 12 -0.523 11
1998 -0.568 11 -0.531 12
1999 -0.525 10 -0.205 10
2000 0.164 9 -0.913 13
2001 0.742 5 -1.612 14
2002 0.961 2 -1.799 15
2003 0.614 8 -0.053 9
2004 0.712 6 0.160 8
2005 0.874 3 0.264 5
2006 0.850 4 0.840 3
2007 0.684 7 1.777 1
2008 1.022 1 1.533 2
Year [F.sub.3] Ranking F Ranking
1994 -0.042 6 -1.377 15
1995 -0.377 10 -1.163 14
1996 -0.203 8 -1.121 13
1997 1.109 3 -0.691 12
1998 0.600 4 -0.468 11
1999 0.128 5 -0.435 10
2000 -0.721 13 -0.030 9
2001 -0.294 9 0.391 8
2002 1.140 2 0.664 5
2003 -0.514 11 0.446 6
2004 -1.801 15 0.444 7
2005 -0.736 14 0.673 4
2006 -0.072 7 0.773 2
2007 -0.635 12 0.699 3
2008 2.418 1 1.194 1
