Section 8 summarises the main findings of the regional benchmarking exercise and
draws out the methodological and policy implications.
2. Innovation and Regional Development
Underlying the regional innovation benchmarks is the evolutionary nature of the
process of innovation (Nelson and Winter, 1982; Metcalfe, 1997). In any area the
nature and direction of technological change will be shaped by the decision rules,
learning capabilities and adaptive behaviour of local firms (Metcalfe, 1997), social
conventions (Morgan, 1997), and the intensity and extent of organisational and inter-
personal interactions (Maillat, 1995; Grabher and Stark, 1997). Regional comparisons
of innovation will therefore depend on the learning capabilities and adaptive
behaviours of individual firms, the inter-connectedness of innovating organisations
and the wider institutional structure which supports the innovative activity of firms
(Metcalfe, 1995, pp. 447-449).
In this view, firms have limited competence or knowledge in the face of a complex
operating environment and so develop routines for decision making (Nelson and
Winter, 1982). Metcalfe (1995, p. 450) then argues that ’important corollaries of the
routine-based approach are the inertial nature of decision rules, their insensitivity to
small changes in the environment and the adaptation of routines as a consequence of
learning behaviour’. In other words, firms have bundles of capabilities or resources
that determine the sophistication and/or effectiveness of their decision making
routines, and perhaps more importantly, their ability to learn or modify routines in
response to their success (Morgan, 1997; Rees, 2000). The presence of an R&D
function within a firm, for example, may stimulate innovation directly through the
type of technology-push process envisaged in linear models of innovation. R&D staff
may also, however, contribute to firms’ creativity as part of multi-functional groups
(Song et al., 1997), or may allow firms to utilise extra-mural networks or information
sources more effectively (Veugelers and Cassiman, 1999)3. Other studies have
stressed the potential importance of high-grade human resources for generating
3 Relatively few empirical studies have until recently included variables explicitly reflecting the skill
composition of firms’ workforces as a determinant of innovation. See, for example, the material
reviewed in Cohen (1995) and the papers included in Kleinknecht (1996). More recent studies based on
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