Innovation and Networks in Industrial Clusters: Empirical Results from Three Large Cities - Technopolis: Best Practices for Science and Technology Cities - page 256

industrial system has a key role in influencing the innovative behavior of the firm
(Westhead and Batstone 1998 ). The institutional approach examines the rela-
tionships between national institutions of finance, education, law, science and
technology, corporate activities and government policies, and their influence on
the propensity for innovation (Nelson 1993 ). The relational approach analyzes the
nature of business and social relationships in nations, manifested, for example, in
the way user-supplier links encourage shared learning (Lundvall 1992 ).
Innovation is not an activity of a single firm; it increasingly requires an active
search involving several firms to tap new sources of knowledge and technology,
and apply these to products and production process (Guinet 1999 ). In other words,
innovation is a result of an interactive learning process that involves often several
actors from inside and outside the companies (EC DG XIII and XIV 1996 ; Simmie
2001 ). The current focus on knowledge has combined with the interactive theory
of innovation-led to the analysis of specific factors which determine successful
innovations or which influence the absorption of knowledge created outside the
firm (Schibany and Schartinger 2001 ).
The ''cluster concept'' is one of these factors studied by the OECD focus group.
According to the OECD report ( 1999 ), clusters are networks of interdependent
firms, knowledge-producing institutions (universities, research institutes, tech-
nology-providing firms, etc.), bridging institutions (e.g., providers of technical or
consultancy services), and customers, linked to a production chain which creates
added value. The main idea of a cluster is that it is considered to be better equipped
to succeed in the market place than an isolated company. The 'agglomeration
externalities and positive feedback,' or that enterprises within industrial clusters
have advantage in terms of growth speed or innovation over those that are iso-
latedly located, have been verified (Swann et al. 1998 ).
The enterprises within clusters are located within close proximity to each other;
for this, the search cost of customers and relevant enterprises can be reduced.
Further, face-to-face contacts appear to be very important as sources of techno-
logical information and in the exchange of tacit knowledge. Spatial proximity
greatly enhances the possibility of such contacts. Geographical proximity is typical
of clusters-although it is not absolutely necessary (Rouvinen and Ylae-Anttila
1997 ). Because the cooperation between actors enhances mutual trust, this
industrial agglomeration of producers, customers, and competitors promotes effi-
ciency and increases specialization.
Learning through networking and by interacting is seen as the crucial force
pulling firms into clusters, and the essential ingredient for the ongoing success of
an innovative cluster (Breschi and Malerba 2001 ). The ways enterprises learn in
clusters are by embracing user-producer relationships, formal and informal col-
laborations, inter-firm mobility of skilled workers, and the spin-off of new firms
from existing firms, universities, and public research centers (Breschi and Malerba
2001 ). In particular, universities and research institutes, as producers of new
knowledge, may play a crucial role.
According to Asheim and Cooke ( 1999 ), there are two types of innovation
networks; one is the endogenous innovative network, which is based upon a