Thus, in addition to the above literature on the knowledge production function, we are
influenced by the literature on boundary spanning activities within innovation
(Conway, 1995) and on dynamic capabilities (Teece et al., 1997). All of these are
concerned with the ultimate question of how firms capture value from their own and
others’ knowledge assets.
Section 2 of the paper defines our conceptual model. Section 3 provides an overview
of the study regions and describes our data sources. Section 4 reports the main
empirical analysis and section 5 concludes with a summary and final remarks.
2. Conceptual Framework
Following Crepon et al. (1998) (CDM) and Loof et al. (2000, 2001) (LH) we define
here a model which relates business performance to firms' level of innovation activity.
Implicit in both the CDM and LH studies, however, is the implicit assumption that
undertaking R&D provides a unique route through which a firm may acquire the
knowledge on which to base its innovation activities1. This assumption is contradicted
by much recent evidence, however, which stresses the importance for innovation of
knowledge flows which span the boundaries of individual businesses creating
'extended enterprises' and providing the basis for competition between supply chains.
At the level of the individual business too, inter-company networks (e.g. Oerlemans et
al., 1998) and intra-group knowledge transfers (e.g. Love and Roper, 2001) (LR) have
been shown to have positive effects on innovation outputs.
To take account of the alternative routes by which individual businesses can source
the knowledge inputs for innovation we explicitly allow here for knowledge sourcing
through R&D (RKSi), innovation collaboration along the supply-chain (SCKSI), for
example with customers, suppliers etc. and innovation collaboration with firms and
organisations outside the supply-chain (XSCKSi), e.g. consultants, universities, and
public or private research laboratories.
Following the general line of argument in the innovation production function
literature stemming from Griliches (1989), firms will invest in knowledge sourcing
for innovation only if the expected returns are positive, with the scale of any
investment varying positively with the expected rate of return. Decision-theoretic
models of the choice of research intensity by firms, for example Levin and Reiss
(1984), therefore relate the intensity of knowledge sourcing activity to the expected
post innovation margins, the structure of the industry within which the firm is
operating, the market position of the firm itself, and a range of other firm and industry
specific factors. This suggests that investments by firm i in knowledge sourcing
through R&D (RKSi), supply chain collaboration (SCKSI) and non supply-chain
collaboration (XSCKSi) may be represented by equations of the form:
1. Another possible interpretation of the structure of the CDM and LH models is that
they regard R&D and networking activities as complimentary activities. While
there is some evidence to this effect relating largely to the role of R&D in
expanding firms absorptive capacity (e.g. Harabi, 1997) other evidence points to
the possibility that networking may be a substitute for in-house R&D investments
(e.g. Love and Roper, 2001).