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does not necessarily increase their innovative performance, and may possibly even harm
it (Boschma, 2005; Broekel and Meder, 2008). We refer to this as the proximity paradox,
and claim that it depends on the (optimal) level of proximity between agents whether
their connection will lead to a higher level of innovative performance or not.
The third topic deals with the long-term dynamics of networks and the changing role
of proximity dimensions in the formation and performance of innovation networks. For
instance, do networks of innovation become less geographically proximate over time
during the course of an industry lifecycle (e.g. Ter Wal, 2009)? And when does the evo-
lution of a network structure show tendencies of path dependence, and why? We argue
that the dif erent proximities may induce path dependence in network evolution, and
may cause retention in the local network (Glückler, 2007). We explain how local network
retention might lead to regional lock-in. Last but not least, we argue that a dynamic
network approach should account for that fact that the evolution of network structures
may, in turn, af ect the degree of the dif erent forms of proximity (Menzel, 2008; Ter Wal
and Boschma, 2009b).
2. Network structure and the proximity concept
A key question in (innovation) network research is to explain the presence or absence of
network relations between organizations, or, more generally, the number or strength of
relationships between actors in a network. The dependent variable is thus the bilateral
relation. The main strategy to explain network structure, then, is to compare the similar-
ity between actors that are linked with the similarity between actors that are not linked.
For example, social networks are generally structured along the lines of gender, ethnic-
ity, age and education as people have a bias to make friends of the same sex, ethnicity,
age group and education level. Sociologists call similarity in attributes of nodes
homoph-
ily
, but we follow the terminology of
proximity
, which is more common in innovation
studies and related areas with a focus on inter-organizational networks (Boschma, 2005;
Knoben, 2007; Lagendijk and Oinas, 2005; Rallet, 1993; Rallet and Torre, 1999).
Though scholars dif er in the dei nition of proximity and the number of proximity
dimensions, we follow Boschma (2005) in his dei nition of i ve forms of proximity: cogni-
tive, organizational, social, institutional and geographical proximity. In short, cognitive
proximity indicates the extent to which two organizations share the same knowledge
base; organizational proximity, the extent to which two organizations are under common
hierarchical control; social proximity, the extent to which members of two organizations
have friendly relationships; institutional proximity, the extent to which two organiza-
tions operate under the same institutions; and geographical proximity, the physical
distance or travel time separating two organizations. These proximity dimensions are
discussed below in more detail.
We believe the proximity concept is part and parcel of an evolutionary approach. In
an evolutionary approach, i rms innovate in areas close to their current cognitive capa-
bilities along well-dei ned technological trajectories (Nelson and Winter, 1982). Their
distinctive capabilities also constitute the primary determinants on partner selection in
innovation networks. To exchange knowledge and develop innovations, networking
i rms tend to be close, yet complementary in cognitive/technological space. The concept
of proximity in i ve dimensions as dei ned by Boschma (2005) can thus be regarded as an
extension of the evolutionary approach that focuses on cognitive proximity primarily:
