Geography Reference
In-Depth Information
been a stronger factor in its development, aided increasingly by institutional and organi-
sational learning. From an evolutionary perspective, i rms learn from their own experi-
ence but also from other i rms and organisations they interact with and with whom they
exchange knowledge both organisational and technological. Evolutionists recognise
i rms have histories, path dependencies and development trajectories. They may show
capabilities of survival and prosperity in maintaining a relatively unchanging market
location, as was the experience of many banks, for example, or they may have a special
capability of transforming themselves to i t new market locations by virtue of their fore-
sight competence, like DuPont or Nokia, as cases in point. The latter explore new paths
of growth to exploit; the former adapt more to new demands by incorporating learned
routines, outsource technological requirements, learn of new opportunities, and adapt
to new constraints. If not, they succumb to competition and become an acquisition,
possibly of some new, more innovative vehicle (e.g. private equity business), and if they
cannot evolve to meet new market exigencies they exit the market.
As this chapter focuses empirically on some evolutionary learning characteristics of
clusters, it is worthwhile postulating some possibly original dimensions dif erentiating
a more neoclassical from an evolutionary line of reasoning and hypothesising between
the two. Here we refer to the dif erent perspectives on the role of knowledge spillovers
in the emergence of clusters. Keep in mind clusters are more than agglomerations of sec-
toral neighbours in geographic proximity. Connectivity through communication, trust,
reputation, favour-exchange and other forms of collaboration is involved, as shown in
the empirical section of this chapter. Knowledge spillovers are part of the adhesive in
these arenas of high social capital but also competition. The neoclassical approach to
knowledge spillovers and clusters as drivers of growth is represented in the Marshall-
Arrow-Romer position that privileges
specialisation
of knowledge and expertise as the
growth driver. Hence the fewer inter-sectoral knowledge spillovers that reduce the ef ec-
tiveness of absorptive capacity the better. Single clusters in a possibly random 'darts in a
dartboard'-like space would be consistent here.
An evolutionary approach would be more akin to Jacobs (1969) and her well-known
proposition that diversity is the dynamic driving innovation. From a cluster perspective,
this leads to a hypothesis about cluster mutation as an emergent property of 'Jacobian
clusters'. The evolutionary terminology here would be that of related variety and prox-
imity ef ects hastening lateral absorptive capacity among mature and embryonic clus-
ters. Precisely this phenomenon is found in places like California, North Jutland, Wales
(Cooke, 2008a) and, as Boschma (2005) shows, in Emilia-Romagna. In the former cases
convergence then cluster emergence show specii c path dependence (see Chapter 3 in this
topic) from ICT through biotechnology to clean technology clusters in California, agri-
cultural and marine engineering through wind turbines and solar thermal energy clusters
in North Jutland, and wireless telephony through medical technology to biotechnology.
Predecessor clusters emerged after Schumpeter's i fth innovation category of 'rail-
roadisation' had opened up the respective territories in the 1800s. In Wales agro-food
stimulates emergence of renewable energy cluster emergence (bioethanol and biomass)
while electronics, automotives and aerospace share common photonics spaces, skills and
competences. Cluster-related variety based on engineering skills typii es 'third Italy' clus-
tering according to Boschma (2005). Clearly this is a powerful explanation for regional
evolution and associated policy thinking.
