Combinatorial Knowledge Dynamics: On the Usefulness of the Differentiated Knowledge Bases Model

Abstract

On the basis of existing literature and newly collected empirical evidence this article discusses the theoretical, empirical, research methodological and policy implications of a recently introduced knowledge taxonomy, the so-called Differentiated Knowledge Bases model (Asheim et al., 2011), which distinguishes between three epistemologically different approaches that are summarized in the notions of analytical (theoretically understanding), synthetic (instrumentally solving problems) and symbolic (culturally creating meanings). The article suggests that these differentiated knowledge bases, though ideal-typical constructs, seem applicable to micro-level, intra- or inter-organizational modes and communities of learning involved in firm innovation but that firms and meso- and macro-level social systems (sectors, clusters, regions, etc.) rarely rely on one single knowledge base but coordinate its actions in more learning modes and communities. The potentials for innovation research of this particular knowledge taxonomy are mainly connected with its integrative and wide perspective on the identification of the types of knowledge, modes of learning and institutional contexts that are relevant for firm innovation and regional economic development and that exceed the sectoral divides and production bias often characterizing innovation research. For innovation policy, this integrative perspective may provide new opportunities for encouraging the development, diffusion and use of economically valuable knowledge of different kinds and from varying societal spheres in ways that truly break with one-size-fits-all policies.

Notes

The EURODITE project, Regional Trajectories to the Knowledge Economy: A Dynamic Model, was supported by the EU Sixth Framework Program, Priority 7: Citizens and Governance in a Knowledge Based Society, Proposal/Contract no.: 006187, Integrated Project (2005–2010).

For a debate on organizations' need and strategies for balancing of exploration and exploitation of knowledge (or “ambidexterity” as this phenomenon is referred to), see, for example, Tushman and O'Reilly (1996), Lavie and Rosenkopf (2006), Lavie et al. (2010) and Raisch et al. (2009).

The retro-perspective of this methodology obviously might result in a certain bias in researchers' selection of “important” steps in the innovation. The “knowledge interactions” selected for investigation in the case studies were not all of the actual steps of the innovating firms but only those that were evaluated as important after the accomplishment of the innovations.

Bogenrichter and Nooteboom's generic notion “learning group” is deliberately chosen as the overall heading here instead of more specific notions such as “community of practice”, “epistemic community” and “professional community” which have different and complementary knowledge and learning connotations, for instance regarding the significance of exploration versus exploitation of knowledge. See Nooteboom (2008) for a comparative analysis of varying learning community concepts. To what extent the differentiated knowledge bases matches the varying categories of learning communities, described in the literature, is not investigated here but remains an interesting research question.

See, for example, Aspers (2004) for an introduction to convention theory not least the French Convention school around Laurent Thevénot, Robert Salais and others. See also Aspers (2009) for an interesting analysis of the role of knowledge, valuation and conventions of buyers and sellers on “standard” and “status” markets.

Although a restrictive strategy of classifying specific statistical occupations as belonging to one of the SAS knowledge bases is generally advantageous, Asheim and Hansen (2009) perhaps are too restrictive in their exercise. For instance, the occupations selected as drawing primarily on a symbolic knowledge base exclusively contains the group of “Writers and creative or performing artists”. Following the prescriptions of the SAS knowledge bases in the literature, other large groups of labour eligible for selection as “symbolic occupations” are journalists, radio and TV producers, graphic designers, advertisement consultants, tourist guides, etc.