Evert-Jan Visser

Learning in districts: Novelty and lock-in in a regional context

This paper deals with the constraints and risks of learning in different types of spatial concentration of related industries and firms. We aim at a better understanding of what makes the difference between local lock-in on the one hand and ongoing creation of novelty on the other. To achieve this purpose, we use Nonaka and Takeuchis (1995) treatment of knowledge conversion processes and Nootebooms (2000) cycle of discovery. Hence, we are able to clarify the concept and nature of learning, which in turn provides a basis for specifying different learning effects of two prototypes of spatial concentration: Marshallian and dynamic industrial districts. We show that these two types of industrial districts have multiple, different, and complementary functions in terms of knowledge conversion and knowledge creation. Hence, we can explain why spatial concentration can have positive and negative effects for learning and innovation, and how lock-in can be avoided.

With or Without Clusters: Facilitating Innovation through a Differentiated and Combined Network Approach

Over the past decades, economic and innovation policy across Europe moved in the direction of creating regional clusters of related firms and institutions. Creating clusters through public policy is risky, complex and costly, however. Moreover, it is not necessary to rely on clusters to stimulate innovation. A differentiated and combined network approach to enhancing innovation and stimulating economic growth may be more efficient and effective, especially though not exclusively in regions lacking clusters. The challenge of such a policy is to mitigate the bottlenecks associated with “global pipeline”, “local buzz” and “stand alone” strategies used by innovative firms and to combine these strategies with a view to their complementarity in terms of knowledge effects. Private and semi-public brokers will be key in the evolving policy, as timely organizational change is crucial for continued innovation, while brokers also need to mitigate governance problems. This requires region-specific knowledge in terms of sectors, life cycles, institutional and socio-cultural factors, and yields spatially differentiated and differentiating adjustment strategies. The role of public policy is to assist in recruiting, provide start-up funding and monitor brokers. With this, policy moves towards a decentralized, process-based, region-specific, spatially diverging and multi-level system of innovation that is geared towards the evolving innovation strategies of firms.

The Complementary Dynamic Effects of Clusters and Networks

Over the past decades, researchers and policymakers around the world have been paying attention to the concept of clusters of related firms, industries and institutions, with a view to the presumably positive effects of clustering for learning, innovation and the productivity of firms. More recently, a network approach to learning and innovation emerged, which emphasizes strategic, preferential, repeated and at the same time temporary knowledge exchange (i.e. dynamic cooperation) between firms and other organizations. This may, however, go at the expense of the attention for the important, different and complementary learning effects of the mainly spatial process of concentration and clustering of related firms, industries and institutions. This paper argues that clusters and networks are two separate concepts that both merit attention, especially—albeit not exclusively—with a view to learning, knowledge development and innovation. A first argument is that spatial clustering has quite different effects for the development of knowledge, learning and innovation in and by firms, as compared with network settings. A second point is that in some cases, clustering yields a governance advantage over networks. Taking into account the risks of cognitive, technological, organizational and institutional lock-in associated with both processes, this paper concludes that both clustering and networking have advantages and disadvantages for the firms involved. The two concepts are potential dynamic complements, as clustering and networking have different but complementary effects for learning, although they are also static substitutes, as firms may opt to switch between the two processes, for example, leaving a local or regional cluster to engage in a network endeavor at a higher spatial scale.