Geography Reference
In-Depth Information
type of regime, such cases generally exhibit low entry rates (high barriers)
and high industry concentration. Large incumbents, often MNEs, account
for most of the sector's innovative activity, and these firms can profit from
their innovations in part because they have the potential to exclude rival
firms from using the new product and processes they have generated. In
these situations, knowledge based on specialized non-transferable experi-
ence is the major input in local innovative activity, and the incumbent
firms tend to have a major innovative advantage over new firms because
innovation is largely routinized and processed within the existing hierar-
chical bureaucracies. As such, leading firms, particularly large MNEs,
play a crucial role, and power asymmetry is central to the value chain and
the governance of innovation in the cluster or region (Cooke 2001).
In knowledge terms the social network model is the third industry-space
typology. However, once the different types of spatial configurations are
distinguished in terms of technological regimes, structures and govern-
ances, as well as their transactional relations, it becomes clear that the
social network model ceases to be a relatively homogenous and consist-
ent single analytical category. Thus, the single transactions costs-based
social network typology previously discussed in Table 5.1 can now be split
into two subcategories, namely the 'competence-based social network'
category and the 'trust-led social network' category. Such a distinction
is based on the dominant features of the technological regime which are
likely to prevail in each of the two social network types. In the case of the
competence-based social network the technological regime is entrepre-
neurial, science-based and technologically widening. In the case of the
trust-based social network the technological regime is routinized, techno-
logically cumulative and deepening. Here, we therefore move well beyond
the innovation adaptation of Table 5.1 described Iammarino and McCann
(2006) which treated the social network model as a single category,
whereas all of our arguments here point to two very distinct categories of
social networks.
In the competence-based social network model high technological
opportunities come primarily from sources outside of the firm and the
industry sector, such as university academic research. In this kind of tech-
nological environment the type of knowledge tends to be both generic and
non-systemic, with high rates of market entry and exit, a strong degree
of volatility of market shares, and low levels of market concentration. In
such an environment, the tacit and sticky nature of knowledge requires
geographical proximity, particularly in the early stages of the industry
or cluster life-cycle. On the other hand, the openness of the innovation
system, the relative 'leaky' character of new knowledge and consequently
the high potential for spillovers, and the related emergence of new rules,
