Geography Reference
In-Depth Information
Table 12
.
4
Structural indicators of knowledge and business networks
Colline Pisane
Bolgheri /Val di
Cornia
Valle de
Colchagua
Knowledge network
Density
0.04
0.05
0.09
Hirschman/Heri ndahl on
actor coreness
0.311
0.091
0.046
Fragmentation -
0.919
0.878
0.690
reciprocated ties only
Fragmentation -
0.756
0.395
0.442
unreciprocated ties also
Business network
Density
0.32
0.20
0.30
Hirschman/Heri ndahl on
actor coreness
0.010
0.014
0.012
Fragmentation
0.063
0.049
0.123
edge networks may af ect the degree of unevenness of cluster i rm performance. It is
striking that the presence of local business networks is instead not found to have an
impact on this. As Table 12.4 shows, the business networks are structurally similar across
the three clusters and, overall, they are denser and less fragmented than the knowledge
networks.
On the basis of these results, it is possible to consider that, at least in these three clus-
ters, the presence of pervasive and relatively dense business networks does not translate
into benei ts for the community of i rms located in the cluster. It seems plausible to argue
that this may be a result of the
content
of the ties of this network, determined by the
qualitative nature of the relationships (Gulati, 1998; Rodan and Galunic, 2004). In spite
of their pervasiveness, business linkage may not be channels for the transfer of valuable
knowledge or be the vehicle of any other pecuniary advantage. For example, belonging
to the same business association or interacting in a trade fair may spark new ideas and
opportunities when i rms already have internal capabilities to take advantage of the
interaction. Otherwise, the potential advantage of an interaction is left in a vacuum.
This clearly indicates that the characteristics and behaviour of i rms play a central role in
determining the benei cial ef ects of network embeddedness.
For this reason, knowledge networks appear to have a more powerful impact on the
performance of i rms. The selectivity through which they are formed suggests that i rms
with stronger knowledge bases and more advanced absorptive capacity are more likely to
participate in the local knowledge network (Giuliani, 2007a). This in turn may guarantee
a certain degree of quality in the
content
of the knowledge transferred, because this type of
i rm is more likely to transfer high quality knowledge about, for example, frontier methods
of production. The downside of this is that the selectivity of the knowledge network
may signify higher returns/performance exclusively for the i rms that are connected to
the knowledge network and no ef ect on those cluster i rms that are either excluded or
peripheral in the network. By way of this, i rms that are centrally positioned in the cluster's
knowledge networks may improve their position and performance in a self-reinforcing
