Geography Reference
In-Depth Information
partners (i.e. the formation and dissolution of ties) based on proximity. This means
that i rm dynamics are a basic input to understand the spatial formation of a network.
As Klepper (2007) and others set out, spinof dynamics is a crucial determinant of the
location of industries, often leading to spatial clustering. In that respect, the emerging
innovation network is most likely to cluster spatially as well, if not only because social
relationships are established through the spinof process between the parent organiza-
tion and its of spring (i.e. the new spinof companies).
Taking the industry lifecycle model as a point of departure, one can start to theorize
about the network dynamics that follow. Studies have shown that after the creation of
a new industry the number of i rms i rst grows rapidly, then falls rapidly again (called
a shake-out), and eventually stabilizes into an oligopolistic market structure domi-
nated by a few persistent industry leaders (Klepper, 1997; Klepper and Simons, 1997).
Furthermore, the spatial concentration of the industry tends to increase over time as suc-
cessful parents create more, and more successful, spinof s, which locate near their parents.
After the shake-out, the i rms that typically survive are indeed a few early entrants and
their spinof s. Apart from the famous case of spinof s in Silicon Valley, examples can be
drawn from the US and UK car industries (Boschma and Wenting, 2007; Klepper, 2007)
as well as from the US tyre industry (Buenstorf and Klepper, 2005).
From the industry lifecycle pattern, we can derive propositions about the patterns of
network evolution that are most likely to emerge (see e.g. Menzel and Fornahl, 2007; Ter
Wal and Boschma, 2009b). First, as the knowledge base of an industry is progressively
codii ed, the geographical distance of network relations is expected to increase over time
(Menzel, 2008). This has indeed been observed in German inventor networks in the bio-
technology sector (Ter Wal, 2009). Second, one can expect the probability of surviving a
shake-out to be dependent on the degree of a i rm in the inter-i rm network. This means
that the average degree of i rms increases over time. Third, given the second proposition,
the falling number of i rms implies that the density over relations increases over time.
Fourth, as spinof s typically have a high degree of proximity with their parent i rms in
the cognitive, social and geographical dimensions, network relations between spinof s
and parents i rms are much more likely than any other network relation type. The result-
ing geography of networks is, on the one hand, characterized by an increasing number of
local links between spinof s and parent i rms in the same cluster. At the same time, one
expects an increasing number of global links as a result of the increasing codii cation of
knowledge. Thus, even though globalization of networks is expected to occur, the local
density of network links is also expected to increase over time.
The industry lifecycle perspective can thus explain that the high density of network
relations within clusters may become excessive as time passes by. As the number of i rms
falls over time, the remaining i rms are typically embedded in strong social networks
and interlocking corporate boards, which tend to resist structural change in the face
of a crisis. Such resistance can be reinforced by increasing organizational proximity
between i rms through mutual i nancial participation between cluster i rms as well as
by higher levels of cognitive proximity between cluster i rms, resulting from the long-
lasting interactions in the past. According to Grabher (1993) and Hassink (2005), such
structures typically explain the inabilities of old industrial regions to successfully renew
themselves. The solution to such regional lock-in phenomena clearly lies in trying to re-
organize network relations such that interactions can take place between actors that are
