Geography Reference
In-Depth Information
explain i rm and sectoral patterns of technical change and innovation among innova-
tive manufacturing sectors - have adapted the original framework to take into account
structural changes, such as the emergence of information-intensive and life science-
based i rms, the increasingly blurred boundary between manufacturing and services,
or the shifting of sectors among dif erent patterns (see among others, Archibugi, 2001;
Evangelista, 2000; Malerba and Orsenigo, 1996; Marsili, 2001; Pavitt et al., 1989).
More recent research, focusing on the determinants of cross-sectoral dif erences in
agglomeration forces and dynamics, has emphasised the role of technological and learning
processes that are 'likely to af ect the relative importance of phenomena such as localised
knowledge spillovers; inter- vs. intra-organizational learning; knowledge complementa-
rities fuelled by localised labour mobility; innovative explorations undertaken through
spin- of s, and more generally, the birth of new i rms' (Bottazzi and Dindo, Chapter 24 in
this topic). Therefore, if we are to better relate questions of innovation to the emergence
and evolution of clusters and regions, it is necessary to map knowledge and technology
characteristics onto the simple transactions costs models described in Table 8.1.
The reason for this is primarily that the changes in the nature of knowledge and the
emergence of new technical capabilities have been responsible for the transformation
of the nature of the MNE from technology transferor to technology creator, and will
determine how the logic of an MNE, as well as of a type of spatial agglomeration, will
evolve over time.
5. An extended technology-based classii cation of spatial agglomeration variety
As is highlighted in the previous section, once we allow for a broader dei nition of knowl-
edge, we can reasonably assume that the nature of agglomeration ef ects is likely to be
highly sensitive not only to the industry structure, but also to the stage of product and
cluster life cycle, and to changes in the underlying technological base (Audretsch, 1998;
Boschma and Frenken, 2003; Breschi and Malerba, 2001). Several studies have empha-
sised the importance of specialisation in mastering a common knowledge set at the early
stages of industry and cluster life cycles. Alternatively, diversity in complementary sets
of competencies and knowledge has been argued to be more advantageous in later stages
of growth of i rms and local contexts, when interdependent pieces of knowledge have to
be integrated and recombined to sustain innovation rates (see among others, Arora and
Gambardella, 1994; Feldman, 1999; Frenken et al., 2007).
The evolution of i rms in a specii c industry and in a specii c cluster is mainly shaped
by the underlying knowledge conditions, the so-called 'technological regime', that is,
a particular combination of appropriability (of the returns of innovation) conditions,
technological opportunities (likelihood to innovate, given investments in research),
degree of cumulativeness of technological knowledge (extent to which the amount of
innovation produced in the past raises the probability of current innovation) and char-
acteristics of the knowledge base (type of knowledge on which i rm's activities are based).
Technological regimes identify common properties of innovative processes in dif erent
sets of production activities, thus helping to explain asymmetries in the dynamics of
industrial competition at sectoral and geographical level (among others Bottazzi et al.,
2002; Dosi, 1988; Malerba and Orsenigo, 1995, 1996; Marsili, 2001; Nelson and Winter,
1982, and Bottazzi and Dindo, Chapter 24 in this topic).
The shifts of technological regimes are also likely to have a geographical dimension,
