knowledge, as well as different codification possibilities and limits, different
qualifications and skills, reliance on different (regional) organizations and institutions,
and contrasting innovation challenges and pressures.
An analytical knowledge base is found in industrial settings (e.g. genetics,
biotechnology, and general information technology) in which scientific knowledge,
represented in universal and codifiable categories, such as scientific communities and
patent systems, is crucial. Companies in these settings typically have their own R&D
departments, but also rely on research results obtained by universities and other
research organizations; university-industry links and associated networks are
important and more frequently encountered here than among companies with a
synthetic knowledge base. Importantly, knowledge inputs and outputs are often codified
in clusters relying on an analytical knowledge base. This does not make tacit knowledge
irrelevant, but codification is more common in such clusters for several reasons:
. knowledge inputs are often based on reviews of existing studies;
. knowledge generation is based on the application of scientific principles and
methods; and
. knowledge processes tend to be more formal than informally organized (e.g. in
R&D departments) and often documented in reports or files.
A synthetic knowledge base is found in industrial settings in which innovation occurs
mainly through the application of existing knowledge or through new combinations of
existing knowledge. These processes often unfold via local trial and error processes
and are contingent on practice-based interaction in which knowledge emerges in
relation to the practical problems encountered. Most clusters will exhibit traces of both
synthetic and analytical knowledge bases. However, one form is likely to be dominant,
and therefore more powerful in reflecting empirical phenomena.