The value of clustering appears to be linked to the access to specialized services and resources offered to firms and other organizations (DRI/
McGraw-Hill, 1995). There have been attempts to develop inventories of what these resources are and where they are located (Enright, 2000). Listing the assets available to and used by members of a cluster is a prerequisite for understanding how it functions. Key assets include education programmes that match the workforce requirements of the cluster, consultants that are familiar with the cluster’s industries and the R&D that is relevant to the cluster. They also include the freight- forwarders and exporters who know the markets; the banks and accountants who have developed relationships with the cluster; and the trade, labour and professional associations that provide the networking opportunities.
Knowledge assets that are deployed strategically within a region become a source of competitive advantage for that region. The I-Space model (see section 4.3 above) can facilitate the strategic analysis of a region’s portfolio of knowledge assets. These can be located in the I- Space as a function of how codified and how diffused they are. The higher up the I-Space the knowledge asset is located, the more directly usable it is and, hence, the greater its potential economic value. The further to the left in the I-Space it is, the scarcer it is and, again, the greater its economic value (see Figure 12.7). The strategic challenge for a region is to invest selectively in a portfolio ofknowledge assetsthat, over time, can be moved and maintained in the upper left-hand region of the I-Space. This requires a mastery of the social learning cycle with respect to its knowledge assets.
Figure 12.7 suggests that as knowledge becomes more codified and widely diffused, clusters of economic and social activity are likely to become more attractive and have a stronger competitive position. In order for these circumstances to prevail, it is essential that the value of knowledge assets be taken into account by stakeholders in a region.
A major challenge in this area is to examine clustering in order to develop an insight into the real interdependencies, i.e. the cluster dynamics, that produce the flows of ideas and innovations and create the synergies. Research so far has generally examined samples of network relationships, to establish, for example, to whom companies turn for help with business problems; where they could go to see benchmark practices; what services and resources they regularly use; who they trust sufficiently to collaborate with; in which business or professional associations they are active; or perhaps on what advisory boards or councils they serve, see Macdonald (1992, 1998), for example. Clusters depend on relationships and connections and it is to be expected that growth nodes will exhibit features and systematic relationships based on increasing levels of trust. The role of trust is likely to vary in the case of three main types of relationships. The first type of relationship is with the specialized services and resources and the labour pool available, which normally involves contracts and
Growth-nodes in a knowledge-based Europe: a research roadmap 189
therefore calls for the lowest level of trust. The second type is the set of transactions conducted among local firms associated with buying and selling products or services. The third type is untraded transactions which involve sharing information, experience and tacit knowledge. This reciprocal relationship often results in innovation and requires a high degree of trust.
The easiest relationships to map are the sector-based supply chains, for which data are typically available from government agencies. The more difficult relationships to map are the supplier and institutional relationships. These require knowledge about the sales of products and services and the location of specialized support functions. Most mappings are very general, showing cluster members as boxes but giving little precise information about the strength of the linkages. The most difficult relationships to map, but the most interesting, are the flows of tacit knowledge and innovation (Granovetter, 1985, 1995). This mapping requires information from individuals about forums for associative behaviour and the conduct of their professional relationships.
If members of business associations can be identified and special resources and services can be inventoried, then it is possible to approximate their relationships and to map clusters in much greater detail than is possible using other methods. The most common map is a flow diagram in which boxes symbolize key parts of the cluster, the companies, suppliers, services, supporting institutions, and trade, business and labour associations. Connections are often represented by directional arrows. Sometimes the thickness of arrows is used to indicate the intensity of the linkages.
By mapping the intensity of connections, it is possible to examine how tightly clusters are bound internally; the degree to which any Figure 12.7: Valuing knowledge assets in the I-space.
Codified (explicit) Uncodified (implicit/tacit) Undiffused Diffused Attractiveness Competitive position (High) (Low)