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Purpose – The purpose of this paper is to categorize industrial clusters, and then compare three industrial clusters of three countries from the perspectives of hard environment, soft environment, factors from supply and demand sides, and the network mechanism. Design/methodology/approach – Data were collected through interview with cluster coordinators. Qualitative case studies were conducted. Findings – The center of excellence behaves well in nearly all aspects, while the spatially narrowly distributed specific center of innovation mainly exploits benefits from its concentrated sector. For the Chinese comprehensive technology incubator, relatively limited geographical space and broad sectorial distribution endow it with unclear strengths, implying the inadequacy of interconnectedness and industry relatedness mentioned by Porter. Research limitations/implications – Data were collected mainly from cluster coordinators, implying further data collecting and more comprehensive analysis. Practical implications – It only makes sense to compare industrial clusters that are comparable with each other. Elements must be matched to facilitate the network interactions, and hence the innovation performance of clusters. Originality/value – This paper contributes to the theoretical basis through it analyzing and clarifying the scales to measure industrial clusters, and answers the question: what is the situation of industrial clusters behaving in several aspects including hard environment, soft environment, supply, demand, network interactions and innovation performance?
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Environment, Network Interactions and Innovation Performance
of Industrial Clusters:
Evidences from Germany, Netherlands and China
Yan Zhao
School of Management, Shanghai University, Shanghai, People’s Republic of China
Wen Zhou*
School of Computer Engineering and Science, Shanghai University, Shanghai, People’s Republic
of China
Stefan Huesig
Faculty of Business, Economics and Management Information Systems, University of Regensburg,
Regensburg, Germany and
Wim Vanhaverbeke
Department of Business Studies, Hasselt University, Belgium
Purpose – The purpose of this paper is to categorize industrial clusters, and then
compares three industrial clusters of three countries from the perspectives of hard
environment, soft environment, factors from supply and demand sides, and the
network mechanism.
Design/methodology/approach – Data were collected through interview with cluster
coordinators. Qualitative case studies are conducted.
Findings – The center of excellence behaves well in nearly all aspects, while the
spatially narrowly distributed specific center of innovation mainly exploits benefits
from its concentrated sector. For the Chinese comprehensive technology incubator,
relatively limited geographical space and broad sectorial distribution endow it with
unclear strengths, implying the inadequacy of interconnectedness and industry
relatedness mentioned by Porter (1990).
Research limitations/implications – Data were collected mainly from cluster
coordinators, implying further data collecting and more comprehensive analysis.
Practical implications – It only makes sense to compare industrial clusters that are
comparable with each other. And elements must be matched to facilitate the network
interactions, and hence the innovation performance of clusters.
Originality/value – This paper contributes to the theoretical basis through it
analyzing and clarifying the scales to measure industrial clusters, and answers the
* The authors thank the support of the project the Complexity of Cluster Innovation System of Knowledge Intensive
Business Services: Empirical Study based on China, subsidized by Innovation Program of Shanghai Municipal
Education Commission, The authors also thank the valuable advices and deep insights by the editors.
question: what is the situation of industrial clusters behaving in several aspects
including hard environment, soft environment, supply, demand, network interactions
and innovation performance?
Key words industrial cluster, interaction, innovation performance, network
Paper type Research paper
1. Introduction
Nowadays industrial clusters have been considered as one of the most important
channels towards open innovation and economic excellence. There are signs that
suggest industrial clusters might improve economic conditions of a region by means
of gathering firms together and facilitating business transactions. Also, industrial
clusters are often linked with innovation, in developed countries like France,
Germany and US, where many clusters are about high-technology such as information
and communication technology (ICT), bio-technology and nano-technology, or, about
the booming knowledge intensive business services (KIBS), such as software systems,
business consulting, and R&D services. In these particular sector-based clusters,
innovation is inherent. Still another importance of the industrial cluster comes from its
role of employment in a region. Thus, policy makers have been aware of this
phenomenon, and are now trying to find out more evidences, and formulating
corresponding policies and institutions to accommodate the development of clusters.
In reality, plenty of actions are being taken under way. From the perspective of
practices, cluster improving measures are being continually implemented, such as the
traditional Industrial Zones in Italy, Technopoles in France, Bio-clusters in Germany,
as well as the already famous clusters like Silicon Valley and Route 128 in Boston in
US. In emerging economies, great efforts are being made to propel the policy-driven
clusters, especially in countries like China and Korea, where a good deal of industrial
cluster initiatives at the national, regional or local level, are emerging.
From the theoretic or academic perspective, researchers are trying all the way to
understand the cluster mechanisms in terms of operating, factor requirement,
environment, and the impact of the industrial cluster on the local, regional or national
economy. Nevertheless, there still remain vital questions before going further ahead:
do those firms in the industrial cluster behave significantly better than their
counterparts in the outside of the cluster? To what degree does this advantage come
from their reciprocal dynamics? And to what degree does this advantage come from
other factors like environmental issues or cultural issues or infrastructure issues?
Indeed, these are vital, yet considerably difficult questions. Environment and
network interactions both play vital roles in cluster’s innovation performance. On the
one hand, a good infrastructure system, including water, power and gas supply will
benefit the cluster to a significant degree, while good systems of communication and
transportation can also enforce the confidence of cluster managers and employees.
Furthermore, firms in industrial clusters are able to enjoy a sound culture where
creation and “radical ideas” are encouraged and rewarded. On the other hand, there
are several types of interactions and co-opetition among firms, in a formal or informal
way. Managers and employees are provided opportunities to meet in conferences,
seminars and forums, where they can discuss various issues, while cafes and
restaurants and even pubs are also available where they can have a casual chat before
imaginative ideas jump out. The influence of these network interactions upon the
well-beingness of the cluster, such as economic performance and innovativeness,
requires step-by-step studies. What is also worth noticing is that the different natures,
as well as distinguished cultural and social contexts of industrial clusters, probably
play important roles in the pattern and style of the network interactions of the cluster.
This paper, therefore, is trying to explore this question: what is the context of
some industrial clusters behaving in several aspects including hard environment, soft
environment, supply, demand, network interactions and innovation performance? We
mainly focus on the environmental effects, and interactions among the member firms
of the clusters, trying to clarify the related issues from several aspects such as
environment and factors that clusters need, and we will try to compare these aspects
among several specific types of industrial clusters, taking account of different cultural
and social backgrounds. This will provide researchers of industrial clusters and
innovation system with better knowledge of the environmental situation of clusters
and the interactions-based relationship, help to build meaningful mathematical models
to integrate environmental issues and networking issues in details from micro- and
meso- perspectives, and then to quantitatively compute the impacts of these factors
upon the companies’ and clusters’ economic and innovative performances.
2. Environmental issues, factorial issues, networking of the
industrial cluster members, and innovation
2.1 Environment and innovation
Research about the innovation of a cluster, or of a firm which is geographically
embedded into a network, has received much attention recently. When discussing this
phenomenon, much emphasis has been placed on environmental issues, such as the
infrastructure, the management system, the cultural and social context, and so on.
One important research theme is about the connotation of the cluster innovation.
For those clusters which emphasize on innovation, a major function is to enhance the
knowledge creation, storage, flow and application within the clusters.
There are some features of industrial cluster which should not be ignored.
Industrial clusters vary from other organizational forms in the sense that they embody
the situation where multi- enterprises are located in one single district. Clusters
depend neither on solely formal relationships, nor on financial or contractual links,
but more on socialized relationships based on personal interactions, collective
learning, and tacit knowledge flow. This tacit knowledge flow is based on informal
networks embedded in the local clusters. Therefore, R&D activities of the member
firms within the industrial cluster induce knowledge spillover, which in turn helps to
increase the innovation level of the firms and the clusters.
However, besides the network benefits, the members of a cluster are also
confronted with certain problems such as information redundancy (Zaheer and George,
2004; Casper and Murray, 2005), competitive blind spots (Zaheer and George, 2004),
isomorphism (Rocha, 2004; Desrochers, 2001). Researchers use Porter’s model
(Porter, 1990), which is a very often used model, to analyze industrial clusters in
innovation capacity across the Taiwan Strait (Lai and Shyu, 2005), in which context
for firm strategy and rivalry is discussed. Overall, the research summarized above has
provided a valuable basis on which to understand the dynamics of the clustering
process and demonstrated the implicit tradeoffs facing firms deciding to locate within
clusters. Furthermore, the geographical space within which a network is embedded
has a significant influence on not only the current members and information stock of
any network, but also its potential members and information stock (Owen-Smith and
Powell, 2004, Kenney and Patton, 2005).
Therefore, environments, both the hard and soft environment, are important for
providing the backgrounds of the clusters in which they come into being and evolve.
Regional and local environment often provide many factors like “nutritions” for
cluster members to absorb and grow. Here in this research we divide environment into
hard ones and soft ones, considering that some of them are explicit, whether in reality
or can be demonstrate by political or regulatory documents, while other are implicit
but still manifested one way or another The hard environment, comprising local
infrastructure, the local management system, and regulation systems such as tax
regulations and laws, demonstrate the convenience of the facilities, provides
institutional and regulatory elements, which are overt, for clusters’ growth. The soft
environment, made up of human resource availability, social and cultural contexts,
technological potentials, and research funding, on the other hand, supports the clusters
more from the societal and non-systematic aspect, which are invisible. This especially
deserves more elaboration between different countries.
2.2 Factors and innovation
For the factors which are conducive to cluster innovation, the importance of
milieu has received much elaboration, including formal and informal relationships
among cluster members. Collective efficiency, collective learning, localized network,
interactive process, complementarities as well as resource interdependency have been
used to explain the raised innovative capacity of the cluster companies.
The study of economic geography and spatial agglomeration has provided
valuable insights into the dynamics of industrial organization (Swann et al., 1998;
Scott, 2000, 2004). The primary contribution of this field has been the identification
of the centripetal and centrifugal forces driving behavior within industrial clusters and
the effects of these forces on individuals, firms, industries, regions and nations
(Marshall, 1920; Perroux, 1983; Krugman, 1991; Saxenian, 1990; Markusen, 1996;
Martin and Sunley, 2003; Pouder and St John, 1996). Using Porter’s model (Porter,
1990), researchers also analyze factor conditions and demand conditions (Lai and
Shyu, 2005).
Rich inter-firm relationships are primarily driven by geographic vicinity to
competitors, supply chain members and firms in related industries. Proximity
facilitates an increasing number of interactions between related firms, largely as a
function of high spatial concentration (McEvily and Zaheer, 1999; Bresnahan et al.,
Thus, relevant factors are those directly affecting companies’ decision to locate
in the cluster, including demand and supply ones. Factors from the supply side are
those ingredients that assist the enterprises in generating innovative products or
services, by means of providing materials, information, technology or human
resources. They include a good image, good suppliers, access to business information,
access to knowledge outside, approach to research and education institutes, and
training systems. Factors from the demand side, on the other side, refer to the ultimate
motivation sources for the firms to innovate, comprising low cost of searching clients
and convenience of contacts to clients.
2.3 Network interactions and innovation
There have been a number of studies on the interactions among firms and their
impacts on innovation of the companies. Although not all are based on the cluster
perspective, they provide valuable references for the study of the relationship between
cluster innovativeness and member firm interactions. And they’re mainly from
Industrial cluster theory provides one stream of literature background for this
study. Porter (1990) argue that national competitive advantage is constituted by
“home base” conditions that are embedded in localized intrafirm and interfirm
linkages, interorganizational collaboration, and networks. Attention should therefore
be paid to spatially bound “clusters” (Porter, 2000). Malmberg and Power (2005)
claim that there are actually few evidences that firms interact or collaborate more with
other local firms and conclude that “collaborative interaction with similar and related
firms in the localized cluster does not come out as a major knowledge creating
mechanism.” However, this does not necessarily lead to a rejection of locally bounded
theories of innovation (e.g., clusters and regional innovation systems); rather, it leads
to the conclusion that the insights of both approaches need to be integrated more
explicitly in future research.
The industrial cluster approach promotes the idea of studying the interactions
between firms and other organizations, but it largely restricts such analyses to a
particular scale or type of proximity. In contrast, this paper argues that it is important
to explore the concentration and dispersal of innovation processes across multiple
scales (Malmberg, 2003; Malmberg and Power, 2005; Malmberg and Maskell, 2006)
because local external economies from concentration produce both advantages and
disadvantages for firms (Parr, 2002).
Meanwhile, although the industrial cluster approach addresses the specificity of
location issues clearly, it has a tendency toward technological determinism in that
technology is presented as a given to which regions respond. For instance, the
comparison of innovation capacity at science parks across the Taiwan Strait was
largely based on this assumption (Lai and Shyu, 2005). This is not taken for granted in
this paper, as innovativeness, geography wideness and public policies interact with
each other.
Another stream of literature, open innovation, by theory, is almost related to
establishment of ties of innovating firms with other organizations. Since gaining
accesses to local knowledge is of crucial importance in the current knowledge
economy, industrial cluster and regional innovation systems are important for open
innovation because the knowledge flows between cluster companies are crucial
(Vanhaverbeke, 2006). This theory itself includes dyad level and inter-organizational
networks. It is vital for the firms to consider the issues like how to select partners,
how to assess the return and risks of an alliance, how to evaluate the fit between
potential partners and how to structure the cooperative agreement and manage it over
time. As Chesbrough and Rosenbloom (2002) consider the value network as a
function of business model, the value network increases the supply of complementary
goods on the supply side, and can increase the network effects among customers on
the demand side. Furthermore, as the knowledge flows more readily to closer entities
(Jaffe, Trajtenberg and Henderson, 1993), the organizational and institutional
embeddedness of geographically networks might be crucial in explaining the
differences of open innovation in different regions or nations.
Firm theories clarify that the establishment of the boundaries of the firm reflects
the entrepreneur’s preferred but subjectively perceived way of coordinating external
factor markets, internal resources and final customers. Where speed and flexibility are
critical in the pursuit of new business opportunities, the entrepreneur will generally
avoid building resources that are available through market transactions. And it is
likely that new products that are introduced and exploited within the firm will have
comparatively close connections to the existing genetic code, while ideas and
products that have looser connections can be expected to be exploited outside the
firm’s boundaries. Motives for partnership are minimizing the sum of internal and
external transaction costs, rising efficiency. Main factors within a relationship are:
frequency, asset specificity, uncertainty, and complexity (Williamson, 1979). The
geographical proximity can thus provide tremendous convenience for higher
frequency of communication, lowering the uncertainty of transaction because the
adjacency facilitates knowledge flow between organizations, and helps companies to
better control their business.
Also, networks of actors benefit from enhanced information diffusion and their
relatively loose structure facilitates the cross-fertilization of ideas and collaboration,
relative to non-networked actors. This process aids in enhancing the innovative
performance of firms, and provides an alternative to formal collaborative and control
structures. (e.g. research consortia or equity joint ventures). (Feldman, 2003;
Desrochers, 2001; Breschi and Lissoni, 2001; Martin and Sunley, 2003; Stuart and
Sorenson, 2003)
Overall, under many circumstances, cluster provides a good background for
interactions and collaborations among member companies based on interdependence.
Studies are under way to clarify the positive influences of these interactions. But there
are also indications of negative effects. Nonetheless, the patterns of the interaction
remain unclear for most management observers.
In this study, the concept of network mechanisms is used to describe, from
several angles, the phenomenon and trend of the relationship among member firms
within the clusters. These different angles embrace specialization, complementarities,
collaboration, convenience to information, and homogenization.
2.4 Innovation performance
Finally, although there are empirical evidences showing that enterprises in the
cluster might have more tendency of innovation (Baptista and Swann, 1998), the
innovativeness of a cluster as a whole still remains questionable. Apart from the
difficulty of measuring the innovativeness of a cluster, another possible reason is that
most research in this stream tends to adopt the traditional way of observing innovation
from the micro- perspective.
In this study, the concept of innovation performances of the cluster are used to
display how industrial clusters are identified in terms of innovation related activities,
such as patents (applied/authorized) or software copyrights, new products or services,
as well as revenue/profits. All are observed from the perspective of comparison
between cluster member companies and their competitors in the market in general.
3. Features of clusters and the categorization
Clusters at the present days have been promoted extensively, as an important
method of sound innovation and economic performance. Due to different developing
paths, cultural and social contexts and other factors, clusters vary tremendously.
One issue is the spatial scale of the industrial cluster. Interestingly, although it’s
obvious that cluster, by definition, is closely connected with spatial meaning, this
issue of geographical boundary hasn’t received much academic attention as other
aspects. The term “geographical concentration” is vague in that it doesn’t specify in
details to what extent do the firms concentrate. In another word, it doesn’t shed light
on the situations whether firms cluster in one single multi-floor mansion or even a
skyscraper, in their own independent tenements scattered in a land of one square
kilometer, or in a large area where dozens of sizable cities can be counted in. Initially,
Porter (1990) applied his cluster principles to national and international clusters
within industrialized countries, such as Norwegian maritime cluster, but later realized
the relevance for local, regional and state-based clusters (Porter, 2000). Geographic
span of a cluster is affected by the ability of sharing information, resources and
knowledge. Underlying social perceptions, cultural barriers and partiality may also
influence or even limit the size of a cluster (Gibbs and Bernat, 1997). Indeed,
companies tend to rationalize their decisions of locality from economic measurement,
and most factors they consider include the convenience to contact the customers and
suppliers, good infrastructure, availability of human resources, etc. On the other hand,
geography is determined by the distance and time that people are willing to travel for
employment and that employees and owners of companies consider reasonable for
meeting and networking (Rosenfeld, 2002). Just-in time processes, the need for
face-to-face interactions and visibility of regional economies are also highlights
(Anderson, 1994). If one single building, or a small piece of land, whether it’s called
an incubator or science/technology park, located in a metropolis where there are
plenty of resources mentioned before, meets these criteria, it has good potential of
being an attracting cluster. In a larger scale, at the same time, more emphasis might be
put on the availability of the industrial chain, the availability of fund and human
resources of the whole region. Therefore, significant differences should be taken into
account when considering the cluster categorization.
Another topic is about innovativeness of the cluster as a whole. First of all,
industries vary in terms of innovativeness. Those clusters in the so called emerging
sectors with strong innovativeness like ICT, life science, material, and so forth,
develop fairly quickly and have more innovative fruits like patents, new products and
services. The fast growing Knowledge Intensive Business Services (KIBS) are also
behaving considerably outstanding. On the contrary, the traditional manufacturing
sectors are often less dynamic or active in these aspects. Nevertheless, more
integration is being seen between these two types of sectors. The emerging
technologies and new ways of serving clients are mobilizing and improving the
efficiency and effectiveness of the traditional sectors. For instance, nanotechnology
has been applied into classical materials chemistry industry. In particular, the great
advancement and wide application of ICT are utilized as a powerful catalyst in
numerous sectors and enterprises. In some cases, more collaboration about product
and process information sharing, strategic alliance forming and cooperative R&D
projects developing can be found in the emerging high-tech sectors.
A look at the industrial clusters in different economic contexts will discover
vastly different characteristics of industry broadness, namely the industrial boundary
of the cluster. In Europe and North America, the limits of an industrial cluster are
often found to be complying with the standard industrial classification system. There,
clusters are considerably specialized, meaning the relatedness of the members in the
same cluster is high. For them, the relationships within a cluster, including
buyer-seller links, competition, collaboration, and shared-resource relationships, are
vital for innovation breeding and sound economic performance as well (Anderson,
1994). However, their counterparts in China are not like so. Often, the so called “high
tech parks” there are found to comprise more than one specific business line, let alone
some incubators in which ICT manufacturing firms and biotechnology R&D
companies are on the same floor. Under this circumstance, the target market, business
modes and market behavior of these cluster members vary so much that any
meaningful cluster activities like coordination, collective meeting and presentation,
training and membership interactions are less effective, which poses challenges to the
economic performances and innovativeness of these companies.
This paper addresses the comparison between different types of industrial
clusters, taking into account the differences regarding cultural and social contexts.
Thus, these three dimensions are chosen for the mapping of clusters, namely
geography wideness, industry broadness, and innovativeness or added value. The
logic for choosing them is simple. There has been much study on industrial clusters
from the perspective of government’s role and key firm’s status. Clusters are either
classified into spontaneous ones and policy induced ones, on the basis of
government’s dominating style, or into satellite ones and net-like ones, on the basis of
the existence of a key firm. Nonetheless, the study of geographical wideness, industry
broadness and added value has been scarce. Part of the reasons might be most
industrial cluster research is focused on developed economies, where disputes over
these issues are not common because consensus have been reached. Clusters in
developing countries, however, vary greatly from their counterparts in developed
economies regarding these three aspects. There are geographically big clusters such as
Yangtze Delta of China, which extends hundreds of kilometers, while small
incubators are also regarded as a form of cluster at the same time. The innovation
connotations of the clusters are diversified, from the most modern high-tech ICT or
biotechnology to the outdated manufacturing methods. What is more confusing is that
the situations of industrial broadness are quite different from each other. While some
clusters are focusing on one specific sector such as ties, cigarette lighter or shoes, as
seen in many regions in southwest China, many policy induced industrial clusters
such as science/technology parks lack the internal links among member firms.
Sometimes totally different business lines can be found in the same cluster, with each
occupies a sizable portion of the cluster’s output.
There are a good number of types of industrial clusters. To illustrate some of
them, some examples are given as below in Table 1 and Figure 1.
Table 1: categorization of industrial clusters and examples
Type Examples
Manufacturing pole,
Manufacturing cities
Ruhr Area of Germany, Northeast Industrial District of China, Yangtze
Delta of China, Detroit of US
Classical Industrial
Cluster Italian Industrial District, Clothing Clusters in Yangtze Delta of China
Industrial Zone,
Development Zone Economic Development Zones in China
Specialized Park/Area
logistic parks, Shanghai International Automobile City, International Iron
and Steel Service Area of Shanghai, Shanghai Shibei (North) Industrial
Traditional Business Business Incubators, Entrepreneurship Service Centers
City Eindhoven-Leuven-Aachen triangle in Europe
Silicon Valley, Boston Route 128, Bio Region of Germany, Business
consulting cluster in CBD, Financial service clusters in London and New
Yo rk
Development Zone
Science/Tech Parks (City/Country level) in China such as Zhongguancun
Science Park, Zhangjiang Hi-Tech Park, and Hsinchu Science Park in
Taiwan, and Tsukuba Science City of Japan
Center of Excellence
Technopoles in France, High Tech Campus Eindhoven, Chemelot in
Netherlands, Software Technology Park of India in Bangalore,
Zhongguancun Software Park in China
Technology Incubator
High-Tech Entrepreneurship Service Centers, Science/ Tech Parks
(District/University level) in China
Specific Center of
Innovation IT-Speicher, BioPark, Sensorik in Germany
Among these industrial clusters, three types are of particular interest in this study.
They are Center of Excellence, Comprehensive Technology Incubator, and Specific
Center of Innovation. There are several reasons as follows.
High-tech development zones
Industry Broadness
Added Value
Manufacturing Cities
Classical Industrial Cluster
High-Technology Cluster
Business consulting cluster
Financial service cluster
Science/Technology City
Industrial Zone,
Development Zone
Center of Excellence,
Specialized Park/Area
Traditional Business Incubator
Comprehensive Technology
Specific Center of Innovation
Figure 1: mapping of clusters
First, there has been plenty of research about high-tech clusters such as Silicon
Valley and Route 128 (Saxenian, 1994; Bresnahan, Gambardella, 2004; Fallick,
Fleischman, Rebitzer, 2006), and high-tech development zones such as
Zhongguancun, Zhangjiang and Hsinchu (Lai and Shyu, 2005; Tan, 2006), whereas
one vacancy still remains for others which are studied in this paper.
Second, there are several features of these types of clusters. All of them are small
scales geographically. This means these relatively small-scale clusters require fewer
hardware resources such as land and basic utilities, compared with large ones.
Therefore, they are suitable for those high tech R&D SMEs who don’t demand a lot
of those hard resources. This trait bestows them tremendous growth potential in
developed areas where land is rare, or undeveloped areas where infrastructure
building is not that handy. The requirement for soft environment buildings is often
quite the reverse, though, because the innovativeness of these clusters is usually high.
Third, these clusters, though small scale geographically, still present good
examples for observing and comparing the features of network mechanism. Since the
limited space makes it possible to meet with the potential business partners, whether
across the corridor or in the cafeteria, the interactions among member companies,
especially informal ones, and perhaps formal ones as well, might increase. For
innovative companies this makes big sense because they depend greatly upon
knowledge flows to come up with innovative ideas. In this sense, the network
interaction mechanism might have an impact upon the innovation of the cluster.
Furthermore, all three types are likely to follow the pattern of policy-led
establishment and growth. As can be seen later in the cases, all of them are often
“formal” ones in the sense member firms are subjective to the selection of the
dominating organizations, whether it’s a management commission appointed by the
regional government or a key firm. This is quite different from the larger ones such as
high-tech clusters in Silicon Valley and business services clusters in CBD of
On the one hand, for these three industrial clusters, the similarity lies in
innovativeness or added value, because all of them are either about high-technology
such as ICT software, material technology, and mechanical & electrical integration, or
about high value added services such as business consulting, R&D services and
financial services. On the other hand, the differences of industry broadness and
geography wideness provide good perspectives to observe aspects such as
environment and conditions of either supply or demand. Particularly, the extent to
which spatial and industry characteristics are demonstrated might have an impact on
the interaction mechanism within the clusters, and therefore influence the innovation
performances as well. In this sense, the light shed on several dimensions within these
clusters will help to clarify the relationship among them, and the influence on the
innovativeness and economic performances of the cluster later on.
4. Empirical studies: Clusters in the Netherlands, Germany, and
4.1 Backgrounds of three industrial clusters
Previous industrial cluster research mainly focuses on high-tech clusters and
high-tech development zones, which are relatively large in either spatial scale or
industrial wideness. The relatively smaller ones -- Center of Excellence,
Comprehensive Technology Incubator, and Specific Center of Innovation, are
somehow neglected. Trying to fill this gap, case studies of three clusters are
conducted in this paper. The main objective is to clarify and compare several
dimensions of these clusters, taking into account the social and cultural contexts, and
probe into the connections among them and the network interaction mechanisms as
well as the innovativeness of the clusters.
(1) IT-Speicher in Germany
Situated 100 km north of Munich, the Regensburg economic region in Germany
is a fast growing location for ICT businesses. Over recent years some 30 percent of
company start-ups there have been in the ICT sector. The Regensburg IT Incubator
Center, IT-Speicher, is a center for promoting young ICT start-ups and new business
settlements at very high standards. The ICT sector is supported in the Upper
Palatinate by the platform
IT-Speicher is operated by IT Inkubator Ostbayern GmbH, which was
established in 2001. This IT incubator has a total building area of 3,000 m². Forty
companies, thirty of which are high-tech start-ups with their own ground-breaking
products, are currently situated in this start-up center.
Considering the industry broadness and geography wideness, as well as its
innovativeness, IT-Speicher is a good example of specific center of innovation.
(2) Chemelot in the Netherlands
Located in Limburg Province of the Netherlands, less than 20 km away from the
city of Maastricht, Chemelot is in the middle of the so called knowledge triangle
Eindhoven-Leuven-Aachen. It’s not just an industrial park of chemical and materials,
but also a unique community that ensures accelerated business growth through the
open exchange of ideas.
With an area of over 800 hectares, Chemelot is one of the largest chemical
industry clusters in Europe. There are over 60 companies on site, many of whom are
global leaders in their product market combination. There are about 7,500 employees
and more than 30 plants and chemical installations producing together approximately
7.5 million tons of products annually. At the same time, it is also a research &
development campus, with approximately 250 patents produced per year.
Chemelot provides a good example of center of excellence.
(3) Shanghai University Science and Technology Park in China
Based in the campus of Shanghai University, the SUSTP is derived from an old
technology park built in 1991, and is one part of the “One Zone, Six Parks” in
Shanghai High-Tech Development Zone. The SUSTP administrative commission is
fully responsible for operating and managing the technology cluster.
There are over 10,000 m² of a total building area in SUSTP in year 2009, and
over 80 companies are located inside, which ranges from ICT, Mechanical &
Electrical Integration, new materials, environment protection, life sciences, and so on.
It also has a specialized incubator for multimedia, integrated circuit, nanotechnology,
and glass art. Overall, about 700 staff is working in it for entrepreneurship.
SUSTP is a good example of comprehensive technology incubator.
4.2 Discussions
This paper tries to clarify and compare several dimensions of these industrial
clusters, taking into account the social and cultural contexts, and to expatiate
similarities and differences of environment, factors and interactions among different
types of clusters. On the basis of that, attempts will be paid to probe into the
connections between them and the innovation performance of the clusters.
To study these three industrial clusters, interviews with cluster coordinators and
related regional and local officials were conducted from May 2008 to April 2009.
They are chosen for this interview because, first, they are the ones who have the best
knowledge of the overall situation of the industrial clusters we examine; second, it
provides another perspective to observe the industrial clusters other than the
traditional perspectives of company managers, which will complement the old
knowledge and therefore offer a complete picture of industrial clusters. During these
interviews some closed and open questions were asked and answered and records
were taken. For most situations, interviewees were asked to give a mark ranging from
0 to 6, which show their opinions from “strongly disagree” to “strongly agree”. The
questions are about the aspects of hard and soft environment, factors from the supply
side and the demand side, network mechanisms, and innovation performance. Also
those factors which were involved during the interviews are activities the clusters
offer to the member companies. But they show no close link with the other aspects
above, and are therefore not analyzed. All the above questions are related with
industrial clusters and networks, especially from the geographical and cooperative
perspective. Same procedures and patterns of interview were adopted respectively in
Regensburg of Germany, Limburg Province of the Netherlands, and Shanghai of
China, to ensure the comparability of the three clusters.
different cluster characteristics between IT Speicher,
Chemelot and SUSTP
(1) Dimension 1: Hard Environment
There are three items in dimension 1, including local infrastructure, local
management system, and regulation systems such as tax regulations and laws.
The scores of this dimension are the same (5.7) for the German cluster
IT-Speicher and Dutch cluster Chemelot, and is a little lower for the Chinese cluster
SUSTP (5.5). For the center of excellence and specific center of innovation, the
geographical wideness expands along with industrial broadness. This phenomenon
takes place only if the local management systems, whether legislative, governmental
or juristical, evolve into a higher level so that there are proportionate basic
infrastructures and hardware facilities for the governance of the cluster. For a cluster,
a relatively wide geographic area generally corresponds to a broad industrial spectrum,
because good complementarities need to be achieved for a relatively large number of
firms so as to help them realize low cost of searching for clients and convenience of
finding suppliers. This doesn’t apply to comprehensive technology incubators, though.
Most technology incubators in China are rather broad-banded in terms of sectors. And
the physical infrastructure, including network connections, water and electricity
supply, and presenting and exhibition rooms, has to keep up with the expansion of the
industry latitude, which brings difficulty because of the confined geographical space.
The management systems, as well as tax regulations are also confronted with
difficulties when trying to accommodate the same policies to different firms in
Figure 2: different cluster characteristics between IT-Speicher, Chemelot and SUSTP
different industrial contexts.
(2) Dimension 2: Soft Environment
There are four items in dimension 2, namely human resource availability, social
and cultural context, technological potential, and research funding.
For this dimension, IT-Speicher scores 5.0, while Chemelot scores 5.9, nearly
20% higher than the former. SUSTP gets a score of 5.2. A deeper look shows that the
main discrepancy comes from technological potential and research funding. On one
hand, Chemelot, as a center of excellence, was initially created and is still led (to
some degree) by the large company DSM, which results in cutting-edge and
ever-improving technologies in the chemical domain, and also endows the member
firms better capacity in getting local or state fundings for further research. On the
other hand, in IT-speicher, as a specific center of innovation, there is no leading, or
core, corporations which can be a true kernel of technological inventions or marketing
initiatives, or can provide the whole industrial cluster with technological
infrastructures such as public laboratory, testing center, quality center, etc. Meanwhile,
the ability of the cluster as one single entity to attract funding is limited largely due to
the lack of flagship corporations and hence the cluster image. Similar situations are
found in SUSTP, since the broadness of industrial area of the member companies and
lack of core firms lead to even more disadvantages. On the other side, human
resources and social/cultural contexts are all favorable for these three clusters. All are
enjoying the benefits of being located in, or not far away from, the major regional
central city where they can have good access to abundant human resources like
university graduates or engineers, and which can provide them with rather loosened
and free interaction environment. In all three clusters there are favorable atmospheres
for entrepreneurship and innovation. What is slightly different is that in the two
European clusters more casual working and life style can be found, while in China the
emphasis is always put on hard working and achieving a successful career.
(3) Dimension 3: Network Mechanism
There are five items in dimension 3, which are specialization, complementarities,
collaboration, convenience to information, and homogenization.
Slight differentiation is shown between IT-Speicher and Chemelot, since the
former demonstrates a higher score of 5.3 compared to 5.1 of the latter. The main
difference comes from collaboration and convenience to information. Indeed, the
advantage brought by proximity of locating in the same building or land area is
benefit by those firms in IT-Speicher, which has a smaller geographical wideness and
narrower industrial broadness. Employees and managers of the firms have more
opportunities of meetings or just bumping into each other, having a casual chat or
formal talk, and arranging a business event together. Because there are clear
specifications among companies, complementarities based on task dividing and
project group can be achieved, and thus collaborations can be realized. For Chemelot
as a center of excellence, which has a larger land area, collaborations may not be
gained that easily, because contacts among people are usually restricted in their own
buildings/organizations, and lesser interactions will be likely to occur between
different organizations without exceptional reason such as meetings or parties. Also,
information exchange will take place most likely in official occasions like conferences,
seminars and presentations, but less so unofficially. For SUSTP which gets a score of
only 3.0, however, the situation is considerably different. Although it has a land area
of identical order of magnitude to IT-Speicher, very low network interactions are
detected. Complementarities among member firms are not easily realized probably
due to the different sectoral backgrounds, let alone project based collaborations. There
are recreational facilities like tea houses, cafes and bars at arm’s length, but the mental
orientation of chasing success as well as heavy work load itself prevents the staff from
benefiting from them. Several interviewees even mentioned that they “never went
there because it makes no sense”. Overall, the advantages of being tightly located are
offset by the differed business lines.
(4) Dimension 4: Factors from Supply Side
Supply side factors mainly address the services or resources that the cluster is
able to straightly provide its member companies with. They include a good image, a
good supplier, access to business information, access to knowledge outside, approach
to research and education institutes, and training systems.
There is not much difference between IT-Speicher and Chemelot regarding this
aspect (5.3 vs. 5.4). Considering the industrial cluster as a whole, both have good
images in their industries, and there are abundant suppliers and research institutes
nearby. As far as business information and knowledge are concerned, it makes not
much difference from the size of the clusters since they both represent high level
brands within the industry across the whole region after all, and the flow and
exchange of the information and knowledge are free and plenitudinous, and therefore
those resources can be readily accessed. Yet it is moderately differed for SUSTP
(scoring 5.0). As one part of the so called “One Zone, Six Parks”, it has a good
reputation not only among university science and technology parks, but also in
integrating the resources of the University and Zhabei district. But the situation of
supplier, business information and knowledge are not optimistic, because more
industrial colleagues or competitors along the supply chain are distributed in other
districts of Shanghai, and in most cases the conferences, forums and seminars are
organized within the district. Thus the cluster isn’t able to provide these conveniences
unless it helps its members to attend more local or international seminars/conferences,
and take part in more exhibitions together.
(5) Dimension 5: Factors from Demand Side
When companies have to make decisions whether to locate in a cluster, variables
they consider most frequently are connected with customer, such as the cost of
searching clients, and convenience of contacting clients. They are referred as the
demand side factors.
For IT-Speicher, this condition is not as good as those of Chemelot and SUSTP
(5 vs. 5.7 and 5.8). There are three possibilities to this situation. First, relatively small
size limits its ability of getting in touch with clients. Second, member firms in
IT-Speicher are specified in the very sector of the software industry, which also
restricts its potential number of customers, although it has a good reputation in the
industry. In comparison, the broadness of the industry covering for the other two
clusters brings them relatively ease of searching and locating potential clients. Last
but not least, the lack of a core company in the cluster makes it difficult to combine as
a whole group and enter the market to find the clients, and to circulate any valuable
information regarding this matter. Interestingly, SUSTP behaves well in this aspect,
surprisingly comparable with Chemelot, despite the fact it has no core company. One
possible reason is that Shanghai University, Non-Profit Organization though it
belongs to, is playing a crucial role in assisting the cluster to get in touch, and build
new bridges with potential clients. The University as a high prestigious entity in the
society offers a one-and-only advantage for the technology cluster, and thus forms a
unique business relation advantage for it.
(6) Dimension 6: Cluster Innovation Performance
Innovation performance looks at three aspects: patents (applied/authorized) or
software copyrights, new products or services, revenue/profits. All these factors are
observed from the perspective of comparison with competitors in the market.
Chemelot demonstrates better in this dimension again than IT-Speicher (5.8 vs.
4.5). It has an excellent track record in patents, and sound record in revenues. The
existence of DSM as a core player within the cluster presents a major advantage: it
facilitates the process of inventing and applying for patents, not only providing
infrastructure such as labs, testing center or quality center, but also corresponding
trainings, professional advices and improvement suggestions, which are truly value
added activities for the other smaller cluster members. This might as well be a
source of comparatively higher revenue/profits for the cluster. For SUSTP, the score
of 3.8 really demonstrates the problem of insufficient capability of innovation. The
numbers of patents applied and authorized from its members are small, not
competitive against other firms in the market, and there are not many new products
or services released each year. The revenue/profits situation is reasonably better,
largely owing to the tax/fiscal preferential policies provided by the cluster
administration office and the local government, which is de facto the key
attractiveness for most entrepreneurial companies.
5. Implications and Conclusions
5.1 Implications
Although economic geographers and management researchers began studying
industrial cluster nearly 20 years ago, it still leaves a lot of blank issues. One of the
main concerns is that how cluster members interact with each other, and how this
network mechanism, combined with other factors such as supply and demand as well
as environmental contextual elements, influences the innovation performance of the
whole cluster.
The first implication of this paper is that it only makes sense to compare
industrial clusters that are comparable with each other. That is to say, when talking
about different industrial clusters, there are at least three dimensions to be considered:
geography wideness, industry broadness, and added value characteristics. This
selection criterion should be considered when discussing clusters because elements
and resources might vary too much to make the comparison meaningful.
Second, elements must be matched to facilitate the network interactions, and
hence the innovation performance of industrial clusters. Land area should be matched
to industry broadness. This is specifically meaningful for those small-scale clusters.
This means, while the big high-tech clusters like Silicon Valley represent a way of
integrating a tremendous number of companies in relatively narrow business lines in a
large wide-spread land area, the comprehensive technology incubators, which are
prevalent in developing countries like China, on the other hand, are not good at
encouraging member firms to get integrated into the local network, nor is it easy for
them to improve the innovativeness. This might be because of shortage of
corresponding resources to accommodate so many sectors at the same time.
Synergistic effect can be achieved best perhaps under the circumstances of moderate
similarities between actors, not too much at least. Therefore it is not optimizing to
draw firms of as many sectors as possible into the cluster. It has been common in
OECD countries to build specialized clusters instead of “all in one” clusters. The
importance of having more meetings, seminars, presentations and other formal
occasions of interactions as well as those informal ones such as parties, coffee bar
talks and occasional corridor chats, have been clearly perceived by the cluster
organizers and business. However, for less developed economies this situation has not
been fully comprehended. Many clusters such as science and technology parks and
incubators, even though in the general “high technology” field, still lack the
interconnectedness and industry relatedness mentioned by Porter (1990), due to the
contradictions between a large geographic area and yet a severe shortage of business
5.2 Conclusions
First, this paper deals with the issue about what are the right scales to measure
the industrial cluster? Consistent with instinct, different land area of clusters will lead
to different types and extents of interaction among member firms, and it is also
important to consider the relativity of the business that the companies are running, i.e.
the closeness of their business along the value chain or supply chain. Meanwhile, it is
sensible to divide clusters into a traditional level and a higher-end level according to
the added-value or innovativeness characteristics, so that to demonstrate their
differentiated position in the economic system.
This paper, next, tries to answer the question: (1) what is the situation of a
particular set of industrial clusters, i.e. center of excellence, specific center of
innovation, and comprehensive technology incubator, behaving in several aspects
including hard environment, soft environment, supply, demand, network interactions
and innovation performance? (2) What are the similarities and differences? This
means we are mainly focusing on the interactions among the member firms of the
clusters, trying to clarify the related issues from several dimensions such as
environment and factors that clusters need, and we will try to compare these aspects
among several specific types of clusters, taking into account different cultural and
social backgrounds.
According to the above analysis, it is apparent that the example of center of
excellence, Chemelot, demonstrates fairly good quality in hard environment, soft
environment, demand side factors, and innovation performance. Compared with
IT-Speicher and SUSTP, it shows a kind of “comprehensive boost”, which means it
has no real “short slab” in all the discussed aspects. It enjoys an ample spatial
advantage, and a relatively broad but still well matched industrial span.
On the other hand, IT-Speicher exhibits strongly in hard environment, network
mechanism, and supply side factors. Although spatially narrowly distributed, the
specific center of innovation exploits the benefits from its concentrated sector, thus
facilitating the interactions among its member firms, and encouraging companies to
enter it based on good business essentials like suppliers, information and knowledge
For SUSTP, a typical Chinese comprehensive technology incubator, relatively
limited geographical space and broad sectoral distribution endow it with somewhat
ambivalent conditions. Although it behaves not bad, almost comparative to the other
two clusters in fact, in aspects of hard and soft environment, as well as supply and
demand factors, seldom do its member companies benefit from interactions with their
cluster colleagues like the other two. Collaboration for a project is difficult to realize
because the complementarities are not there in the first place. Due to onerous daily
work and business, the entertainment facilities of arm’s length which are suitable for
unofficial social network activities, are not utilized as much as expected.
Entrepreneurs, managers and employees of different business lines congested in one
single building can hardly produce idealized network interactions.
In summary, the findings of this paper are that the examples of center of
excellence and specific center of innovation in Europe demonstrate relatively better in
network mechanisms and innovation performance than the Chinese comprehensive
technology incubator. Given that the other aspects are slightly different, it might well
be that the network interactions within the cluster has positive connections with the
innovation performance of the cluster. This, therefore, leaves further space for
research in the future.
5.3 Contributions
This paper tries to evaluate some industrial clusters according to a set of criteria.
In order to fulfill this purpose, analyses and categorization of clusters are conducted.
The contribution of this paper includes:
First, this paper contributes to the theoretical basis through analyzing and
clarifying the scales to measure industrial clusters. Situations vary greatly between
mature economies and emerging/undeveloped economies regarding the degree of
government motivation of pushing the industrial cluster, and the degree of perfection
of infrastructure. Spatial scale, relativity of the business as well as the added-value or
innovativeness features are chosen for mapping industrial clusters. This taxonomy
corresponds to the reality that the differences exist in cultural and social contexts,
which might have a significant impact on the cluster’s innovativeness and economic
Second, combining theoretical and practical perspectives, this paper answers the
question: what is the situation of industrial clusters behaving in several aspects
including hard environment, soft environment, supply, demand, network interactions
and innovation performance? Three specific types of industrial clusters are chosen for
comparing according to these dimensions. Mainly focusing on the interactions among
the member firms of the clusters, trying to clarify the related issues from several
dimensions such as environment and factors, this paper takes into account different
cultural and social backgrounds. This will provide researchers of industrial cluster and
innovation system with better knowledge of the relationship based on interactions
within clusters. In particular, these three clusters analyzed are from three different
countries, two from mature economies and one from emerging economy, thus
representing different context. This might shed light on the potential impact of
economic and cultural background upon the mechanism of interaction within cluster.
Third, this paper provides a potential pathway for researchers of industrial cluster
and innovation system to build meaningful mathematical models to measure the
interactions within clusters in details from micro- and meso- perspectives, and then to
quantitatively compute the impacts of these interactions upon the companies’ and
clusters’ economic and innovative performances. Network theories, graph theories as
well as complex system theories are all possible in quantitatively assessing the
development of industrial clusters, whether statically or dynamically.
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Appendix: Case Study Questions
The questions below include closed ones and open ones. For closed ones please give a
mark from 1 to 7. (1: strongly disagree; 2: moderately disagree; 3: slightly disagree; 4:
neutral; 5: slightly agree; 6: moderately agree; 7: strongly agree). For open ones
please give your brief remarks and ideas about them.
Cluster is defined as a geographically proximate network of interconnected companies
and associated institutions in a particular field, including product manufacturers,
service providers, suppliers, universities, and trade associations.
Advices for the filling of the questionnaire
We apply here basically the business organization principle .please also answer the
question, even when it doesn’t deal with an independent firm.
Important indicators of this cluster
Which economic sector is the main business field of this cluster?
What products does this cluster produce and what service does this cluster
Can you tell us the special competences of the products, services or production
method, which are characteristics of this cluster (e.g. special machine or special
1. Environment
1 Hard environment
Do you think the local infrastructure is good for the company’s development?
Do you think the local management system (from the government) is good for the
company’s development?
Do you find a loose regulation (tax and law) in this area?
Do you think the local assessment system (for the company managers and
entrepreneurs) is good for the company’s development?
2 Soft environment
Do you think there is abundant availability of local qualified human resources for
the company’s staff? If so, where are they mainly from? (open)
Do you think the local social and culture environment is good for the company’s
development? Describe them briefly. (open)
Do you think the local technological potential and R&D level is good for the
company’s development?
Do you find it easy to get state or local research funding?
2. Network mechanism
Do you think there is a clear specialization in local companies?
Is it possible for the companies to complement each other’s ability and have a
good cooperation?
Is it easy for companies to collaborate with each other for a project?
Are local cafes, bars or parties important and convenient to access useful business
information, idea, people or other resources?
Are the companies more and more similar to each other in the cluster? (Products
or services, strategy, R&D, etc) If so, what do you think the reasons are? (open)
3. Influencing factors for companies to locate in the cluster
Organizing and managing the local cluster here, to what degree will you agree that the
following factors are good in the cluster?
1 Supply side:
A good image
Enough good suppliers
Can easily access the up-to-date information in this industry
Get more knowledge from other companies such as fellow traders, suppliers and
Can easily approach the research institutes such as universities, colleges, and
research institutes
Good training system
2 Demand side:
Low cost of searching clients
Convenient contacts to clients
4. Is innovation performance of the local cluster very good?
Patents (applied/authorized) or software copyrights
New products or services
Revenues/ profits
Is it long to market the products or service?
5. What activities does your cluster offer, and how often? (Open)
Cluster internal working group
Cooperation with other members in cluster
Information event of the cluster
Qualification offers of the cluster
International measure
Presence in exhibition together
6. Further questions about cluster
a) Is there any elements of which according to your belief the cluster lack. (Multiple
choices possible)
Research Institution Education and Training Workshop
Important Suppliers Important Service Providers
Financing/Venture Capital Network coordinators
b) What are the advantages as a member of a cluster from your perspective?
New contact to R&D cooperation partners
New contact to Suppliers
New contact to Customers
Better access to qualified professionals of the region
Better access to the financial subvention
Better access to credit capital
An image improvement for the own economic field in the space of Maastricht
Better access to information
c) What obstacles and problems do you see in the cluster relevancy?
Too little knowledge about the potential partners in space of Maastricht
No appropriate Partner located in the space of Maastricht
High additional time and coordination’s effort for cluster activities
Too high dependency to partners
Disadvantages through the publication of know-how to the cluster
Too little own utility from cluster activities
d) What characters in your opinion does a successful cluster own?
e) On which field of regional economic politics in your opinion should be
Thank you very much for your work!
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This book was first published in 2004. National economic growth is fueled by the development of high technology clusters such as Silicon Valley. The contributors examine the founding of ten clusters that have been successful at an early stage of growth in information technology. Their key finding is that the economics of starting a cluster is very different from the positive feedback loop that sustains an established cluster. While 'nothing succeeds like success' in an established cluster, far more difficult, risky and unlikely are the initial conditions that give rise to successful clusters. The contributors find regularities in the start of the successful clusters studied, including Silicon Valley around 1964. These cases contain 'old economy' factors such as competencies, firm building capabilities, managerial skills, and connection to markets, more than the flamboyant 'new economy' factors that have been highlighted in prevailing years.
Hot spots are fast-growing geographic clusters of competing firms. Drawing on several literature streams, we develop an evolutionary model that contrasts hot spot and non-hot spot competitors within the same industry. Initially, economies of agglomeration, institutional forces, and managers' mental models create an innovative environment within the hot spot. Over time, those same forces create a homogeneous macroculture that suppresses innovation, making hot spot competitors more susceptible than non-hot spot competitors to environmental jolts.
With the publication of his best-selling books "Competitive Strategy (1980) and "Competitive Advantage (1985), Michael E. Porter of the Harvard Business School established himself as the world's leading authority on competitive advantage. Now, at a time when economic performance rather than military might will be the index of national strength, Porter builds on the seminal ideas of his earlier works to explore what makes a nation's firms and industries competitive in global markets and propels a whole nation's economy. In so doing, he presents a brilliant new paradigm which, in addition to its practical applications, may well supplant the 200-year-old concept of "comparative advantage" in economic analysis of international competitiveness. To write this important new work, Porter and his associates conducted in-country research in ten leading nations, closely studying the patterns of industry success as well as the company strategies and national policies that achieved it. The nations are Britain, Denmark, Germany, Italy, Japan, Korea, Singapore, Sweden, Switzerland, and the United States. The three leading industrial powers are included, as well as other nations intentionally varied in size, government policy toward industry, social philosophy, and geography. Porter's research identifies the fundamental determinants of national competitive advantage in an industry, and how they work together as a system. He explains the important phenomenon of "clustering," in which related groups of successful firms and industries emerge in one nation to gain leading positions in the world market. Among the over 100 industries examined are the German chemical and printing industries, Swisstextile equipment and pharmaceuticals, Swedish mining equipment and truck manufacturing, Italian fabric and home appliances, and American computer software and movies. Building on his theory of national advantage in industries and clusters, Porter identifies the stages of competitive development through which entire national economies advance and decline. Porter's finding are rich in implications for both firms and governments. He describes how a company can tap and extend its nation's advantages in international competition. He provides a blueprint for government policy to enhance national competitive advantage and also outlines the agendas in the years ahead for the nations studied. This is a work which will become the standard for all further discussions of global competition and the sources of the new wealth of nations.
This paper aims to explore the innovation capacity in two different science parks across the Taiwan Strait. In both Taiwan and China considerable resources are being devoted to science parks as policy instruments aimed at promoting R&D-based as well as innovation activities. For this study, we chose the Zhangjiang High-Tech Park (ZJHP) of China and the Hsinchu Science-based Industrial Park (HSIP) of Taiwan to compare innovation capacity. Based on Porter’s (The Competitive Advantage of Nations, Free Press, New York, 1990; Cluster and Competition: New Agendas for Companies, Governments, and Institutions, on Competition, Harvard Business School Press, Boston, MA, 1998; Econ. Develop. Quart. 14 (1990, 1998, 2000) 15) model for the innovation orientation of national industrial cluster, this paper proposes a model to analyze the science parks in innovation capacity across the Taiwan Strait. We 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”.
This paper explores the role of the business model in capturing value from early stage technology. A successful business model creates a heuristic logic that connects technical potential with the realization of economic value. The business model unlocks latent value from a technology, but its logic constrains the subsequent search for new, alternative models for other technologies later on-an implicit cognitive dimension overlooked in most discourse on the topic. We explore the intellectual roots of the concept, offer a working definition and show how the Xerox Corporation arose by employing an effective business model to commercialize a technology rejected by other leading companies of the day. We then show the long shadow that this model cast upon Xerox's later management of selected spin-off companies from Xerox PARC. Xerox evaluated the technical potential of these spin-offs through its own business model, while those spin-offs that became successful did so through evolving business models that came to differ substantially from that of Xerox. The search and learning for an effective business model in failed ventures, by contrast, were quite limited.