Geography Reference
In-Depth Information
neither a very high nor a very low degree of technological proximity between partners
resulted in exploration.
As Boschma and Frenken set out in Chapter 5, the study of dynamic spatial networks
has thus far been limited, because of the limited availability of longitudinal data on
networks and the poor methodological state of dynamic network analysis (see Ter Wal,
2009). Having said that, in the context of evolutionary economic geography, the main
challenge is the study of the dynamics of network formation: how do networks of i rms
arise and develop in time and space, and what forms of proximity are important at what
stage of the evolution of the network? In this context, the focus of attention is on the
dynamics in the number of nodes and relations, and how the dif erent forms of proximity
impact on these network dynamics. What is interesting from an evolutionary perspec-
tive is to examine whether the dif erent proximities induce path dependence in network
evolution, and whether they cause retention in the local network (Glückler, 2007).
Moreover, a dynamic network approach should account for that fact that the evolution
of a network structure may, in turn, af ect the degree of proximity in its various dimen-
sions, like in the social and cognitive dimension (Menzel, 2008). However, this requires
further rei nements at the conceptual and methodological level before this can be applied
empirically (see for example Glückler, Chapter 14).
In Chapter 17, Uwe Cantner and Holger Graf apply a dynamic perspective on regional
innovation systems. Their contribution provides a study on the evolution of the Jena
innovation network based on patent data. Behind the increase of the network size and
the degree of connectedness, considerable dynamics are observed in terms of actors
entering and exiting the network. They demonstrate that permanent innovators and new
entrants show a tendency to concentrate more on the technological core competences of
the network, while the network as a whole shows an increasing inward orientation to the
local level. This chapter gives evidence of the enormous potential of analysing network
dynamics at the regional and local scales, a research i eld that is still underdeveloped.
Taking a dynamic perspective on network evolution, one can also theorise from the
industry lifecycle concept about the role of inter-i rm networking at dif erent stages of
the lifecycle (see Boschma and Frenken, Chapter 5; Ter Wal, 2009). This is not to say
that each industry is destined to follow such a life-cycle, and that each stage follows
the other in a deterministic manner. Already in the 1980s, some economic geographers
challenged the application of the industry lifecycle approach in such a stylised manner
(see for example Taylor, 1986). Moreover, this would go against the nature of eco-
nomic evolution as an open-ended process that is conditioned but not determined by its
spatial context (Martin and Sunley, 2006). However, the main purpose of developing
an endogenous model of network evolution along industry life-cycle lines is to derive
some hypotheses that can be tested case by case, in order to determine the empirical
veracity and applicability of the ideal type, especially in dif erent spatial contexts. Ter
Wal and Boschma (2009) have applied an industry life-cycle framework to the study of
network dynamics. Following the life-cycle scheme of Abernathy and Utterback (1978),
they expect that i rms engage in network activity in the i rst phase to explore the possi-
bilities of a new technology. As technologies are still competing and uncertainty is high,
the network is unstable, with i rms often changing partners. In the second phase, i rms
developing dominant designs become the hubs while lagging i rms try to maintain their
position by networking with a hub (see Orsenigo et al., 1998). In the mature phase, the
