Knowledge, learning and innovation in spatial organisation

The concentration of production within rich socioeconomic networks of firms, adequate supporting institutions and effective local governance generates the platform for knowledge creation, learning and innovation in location (Asheim et al, 2011). Through inter-firm and intra-firm interactions, the firms are able to generate knowledge; thus, they learn in a globalised competitive world’s production system. The role of proximity and local ties in the dissemination of knowledge and innovation has received considerable attention. Asheim (2001) contends that the challenge for bringing location back into economics rests on the learning which is prompted by the location of the firm. Learning, as an innovation process, according to Asheim (2001), is embedded in the history of location. This waters down the linear model of innovation and projects the supremacy of firm clustering as an efficient material context of innovation.

Works on IDs have revealed the relevance of location in the distribution of information and knowledge acquisition and sharing. As a result, two forms of knowledge have been observed in the production process: formal or ‘codified’ and informal or ‘tacit’ knowledge (Oinas, 2000; Helmsing, 2001; Becattini, 2002; Asheim, 2011). Informal or ‘tacit’ knowledge constitutes the knowledge acquired through practise and interaction of economic actors and institutions. It is unstructured, unintentional, not bound by time, and does not lead to certification. Formal or ‘codified’ knowledge, on the other hand, is deliberately acquired through an education programme or in a training institution or accompanying a product or service. It is structured, and it leads to certification (CEDEFOP, 2008). Malmberg and Maskell (1999) and Munari et al (2011) believe that face-to-face interactions exemplify the use of tacit knowledge and information which is transmitted based on mutual trust in the local proximity. The concept of tacit knowledge is associated with Polanyi’s work in the 1950s; Nonaka and Krogh (2009) referred to the concept as a non-coded knowledge which is location specific and thus provides location differences. Scholars have agreed that tacit knowledge is entrenched in proximity relationships and it is transferred between organisations as people move jobs. Moreover, it provides a higher premium (Nonaka and Krogh, 2009; Asheim and Hansen, 2009; Pina and Tether, 2016). Therefore, tacit knowledge together with

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codified knowledge gives a location inimitability in the production system but, since production systems do not exist in isolation, there is the need to innovate if firms are to compete in the global system of production (Todtling et al, 2012; Pina and Tether, 2016). It can be inferred from the discussions above that no two locations can have the same combination of tacit and codified knowledge; therefore, there is diversity in knowledge and innovation with respect to location. Asheim et al (2012) recognise that these elements of regional diversity account for failures in adaptations of the ‘one-size-fits-all’ policy. However, Asheim et al (2012) do not see diversity alone as a sufficient regional development inducement factor; rather, they propose specialisation in technologically-related sectors to induce development in a region as the sectors are able to absorb technological spillover provided they have a similar technological base. The new technologies are transmitted through personal interactions and relationship ties among members in close proximity. Through their network ties, firms are able to access new knowledge and technologies in the production process. By cooperating with each other, actors in these sectors are able to absorb the new knowledge and technologies transmitted (Gallié, 2009; Munari et al, 2011; Asheim et al, 2012; Ahammad et al, 2016). It appears that close contact and face-to-face interaction among economic agents leads to the transfer of valuable ‘tacit’ knowledge which facilitates localised technological spillovers. However, measuring the rate of such spillover remains problematic (Munari et al 2011; Chen and Tan, 2016). The critical question here is: what is the source of this new knowledge and technologies and which economic agents that pursue knowledge mediation function for the local system? Moreover, what are the main bottlenecks in transmitting this knowledge and technology in developing economies?

Oinas (2000) argues that, in a globalised world, knowledge and learning may come through imitation and the creation of entirely new knowledge. Imitation thrives both in proximity and in distance within the right network and place. However, Asheim (2001) argues that imitation based on exogenous knowledge is problematic in itself and it does not represent an innovative region; moreover, there are intangible differences in the adoption and application of technology. He holds that the firm’s competitiveness comes with the creation of new knowledge through searching, exploring and experimentation in the development of new products and processes in consonance with socio-cultural attributes which may create variety in technology and make it difficult to imitate embedded knowledge (Asheim, 2012). Todtling et al (2012) acknowledge the complexity of knowledge creation, learning and innovation by

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arguing that tacit and codified knowledge cannot be assumed as a simple phenomenon since they coexist in different combinations. These combinations are distinguished by ‘analytical’, ‘synthetic’ and ‘symbolic’ knowledge bases (Todtling et al, 2012; Asheim et al 2012):

Analytical Knowledge Base is a codified form of knowledge that requires special training to generate and, as a result, brings about radical innovation. Due to its mode of acquisition, this form of knowledge requires patenting and licensing.

Synthetic Knowledge Base on the other hand constitutes a blend of codified and tacit knowledge targeted at solving specific problems arising in the production process as customer and supplier interaction. Unlike analytical knowledge, synthetic knowledge, though adopt research and development (R&D), generates incremental innovation in existing firms

Symbolic Knowledge Base is distinctive tacit and context-specific knowledge generated as a result of the dynamic development of cultural specific production which incorporates and transmits aesthetic symbols, images (de)signs, artefacts, sounds and narratives with a strong cultural content.

Asheim (2012, p997) argue that “complexity and diversity of contemporary knowledge creation and innovation processes require firms to acquire new knowledge to supplement their existing knowledge by attracting human capital possessing competences based on a different knowledge base”. As a result, knowledge flows can take place between industries with very different degrees of research and development intensity and different knowledge- based characteristics (Asheim, 2012). Though knowledge flow can take place between different industries, the issues of differentiated variety and institutional frame may serve as bottlenecks to the smooth diffusion of the knowledge (Munari et al, 2011). The fact remains that knowledge, be it symbolic, synthetic or analytical, contains institution- or organisation- specific characteristics that may impede the smooth transmission and absorption of knowledge. In effect then, knowledge is either tacit or codified irrespective of the classification.

Helmsing (2001) and other scholars have argued that innovation is the key to competition in global production systems today. Innovation may be incremental, which involves gradual upgrading and learning through interaction of economic agents, or radical, which results in

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immediate adoption of technology (Asheim 2001); moreover, a firm gains from integrating into local, national and international networks and innovation systems. As Asheim et al (2011b) put it: “innovation is about the creation of new products and processes, but to be effective it must draw on the capabilities of regions”. These scholars argue that the factors mediating geographical transfer of knowledge include: the knowledge transfer system; the institutional setting; the financial system; education and training; the availability and mobility of skilled labour (human capital); and public policy. An innovative milieu conducive to innovation and continuous improvement evolves as a result of collaboration with suppliers, subcontractors, customers and supporting institutions in a region (Asheim et al, 2011b). Asheim et al further argue for radical innovation; they state that, despite the cost of and the uncertainty in the adoption of radical innovation, a learning economy is better placed in the long term. However, if a learning economy relies on incremental innovation it will be increasingly difficult for the firm to reproduce and grow in the long term (Asheim et al, 2011b; Coad et al, 2016).

In addition, recent debates on innovation make a strong case for the need to incorporate external knowledge in order to meet global competition (Munari et al, 2011). External knowledge networks serve as the means through which new knowledge permeates a location. Firms build ‘pipelines’ to access knowledge that is not already part of their repertoire. These pipelines are the channels used to access distant relationships which are out of the information and communication ecology created by face-to-face contacts, co-presence and co-location of people and firms within the same place. Bethel et al (2004) argue that, with the build-up of trans-local pipelines, the more information and news about markets and technologies are ‘pumped’ into internal networks the more dynamic the buzz from which local actors benefit. The issue, however, in recent works of Munari et al (2011) and Morrison et al (2012) is the ease of technological diffusion within firm clusters considering that there is unique tacit knowledge associated with these new innovations that may come through the pipelines external to the firm clusters. There is therefore the recognition of knowledge recipient, translators and diffusers of this knowledge (‘gatekeepers’) in absorbing and defusing this acquired knowledge in the buzz (Munari et al, 2011; Morrison et al, 2012). The effective functioning and sustainability of a cluster requires constant flows of knowledge and innovation through the global pipelines in order to augment the local knowledge created (Moodysson, 2008). In theory, this will make clustering of firms in a location competitive in

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the international global market. The issue then is how different locations can harness the external knowledge, especially the informal and formal sectors in developing economies.

In summary, the discussions so far lead to the expectation that knowledge may be distributed through interaction of economic agents in a place. These interactions spur technological spillover in a location. However, to sustain the operations in these locations requires the infusion of new knowledge external to these locations. The external knowledge may be tapped through external ties and transmitted into the place. But there are issues of socio- cultural differences that may impede the smooth transmission in a location.

In the next section, the Porter’s cluster construct is presented to provide a firm-level competitive strategy that can be projected to the regional and national level. This will serve to throw more light on the advantages of firm location. Therefore, the term ‘cluster’ is used to represent the collection of firms in a location. The industrial cluster construct, by its nature, is easily adaptable and is used to represent different spatial agglomerations, which means the development pattern is taken to be universal and the same strategy is applied to different industries in different regions and nations (Martin and Sunley, 2003; Motoyama, 2008). Unlike IDs that are defined by the place (place-dependent path-dependency), the cluster model provides the means of operation in order to be competitive. Martin and Sunley (2003) contend however that there are some general characteristics that are common to all clusters. The friendly writing style of Porter and the cluster’s strategy orientation towards competition defines its attractiveness among policy makers and scholars, (Martin and Sunley, 2003; Motoyama, 2008). The cluster approach provides a more transparent, inclusive and potentially less trade-distorting framework for efforts to strengthen strategic sectors than the prior policies of supporting large and often state-owned firms (OECD, 2007).