1.1 The impact of geography on innovation
Learning is considered as a key concept within innovation system literature. In the late 1980s, Lundvall (1985; Lundvall, Dosi, & Freeman, 1988) and Johnson (Johnson & Lundvall, 1994) introduced the notion of learning by interacting to emphasize the role of geographic proximity in providing a more direct and easy access to information within user-producer interactions (Lundvall, 1985). More specifically, the authors consider learning “a socially embedded process which cannot be understood without taking into consideration its institutional and cultural context” (Lundvall, 1992, p. 1). This is mainly explained by the fact that innovation generation represents a process characterized by low levels of predictability where learning plays a central role in such uncertain process, which in turn explains why complex and frequent communication between the parties involved is highly required, with specific regard to the exchange of tacit knowledge (Nonaka, Takeuchi, & Umemoto, 1996). The importance of geographic proximity in knowledge transfer processes is further emphasized with the introduction of the notion of learning region (Storper, 2005). In this regard, learning is conceived as a territorially and socially embedded and interactive process (Asheim, 1996), able to drive the successful growth and the innovation performance of regions (Cooke, 1992) thanks to the catalyst role of proximity (Coenen, Moodysson, & Asheim, 2004). Networking with other firms and organizations is therefore considered as a “learning capability” (Lundvall & Johnson, 1994), and different kinds of “learning relationships” (e.g. customer-supplier; cross-sectorial interactions) are deemed to be at the core of the innovation process (Johnson & Andersen, 2012).
Another important aspect is that the impact of geographic proximity on innovation-driven learning dynamics varies according to the nature of knowledge and innovation modes. Lundvall and Johnson (1994) grouped knowledge into four economically relevant knowledge categories:
- Know-what, i.e. knowledge about facts;
- Know-why, i.e. knowledge of scientific principles;
- Know-who, i.e. specific and selective social relations;
- Know-how, i.e. practical skills.
(p. 129)
This taxonomy is useful to understand the different channels through which learning takes place. Indeed, while know-what and know-why can be learned through codified information (e.g. by reading books or lectures), the other two forms of knowledge are more difficult to codify and may be required to be transferred through practical experience. Consequently, while know-why and know-what are more typically produced through STE-based innovation (science, technology and engineering), know-how and know-who are generally associated with DUI-based innovation (doing, using and interacting). Following Jensen, Johnson, Lorenz and Lundvall (2007), the STE mode is “based on the production and use of codified scientific and technical knowledge”, whereas the DUI mode “relies on informal processes of learning and experience-based know-how” (p. 680). Main differences between the two modes of learning are shown in Table 1.1.
Asheim and Gertler (2005), building on the concept of learning as an interactive process, introduce a new dimension analytic dimension to the study of innovation processes (i.e. knowledge base Laestadius, 1998), which can be alternatively analytical or synthetic. The analytical knowledge base refers to industrial settings, “where scientific knowledge is highly important, and where knowledge creation is often based on formal models, codified science and rational processes” (Asheim and Gertler, 2005, p. 296), as in the case of biotechnology, information and communication technologies (ICT) and genetics. University-industry networks turn out to be particularly important, as companies tend to frequently rely on results from research institutions for the development of their innovations. The type of exchanged and produced knowledge tends to be codified, and its application gives origin to radical innovation more frequently. Indeed, radical innovation is typically produced when knowledge is exchanged among actors of different nature through inter-organizational relationships and cooperative mechanisms capable of stimulating reciprocal learning and thereby processes of innovation (Capaldo, 2004). Hence, the presence of actors of different nature (i.e. universities, firms, government institutions), presenting different skills and capabilities and diverse backgrounds can boost the creation of radical innovation as far as they exchange non-redundant information.
Table 1.1 STE mode vs. DUI mode
| STE mode (science driven) | DUI mode (user driven) |
|
| Aim: Increase the R&D capacity of the actors in the system and increase cooperation between firms and R&D organizations | Aim: Foster inter-organizational learning and increase cooperation between in particular producers and users |
Typical innovation policy:
Increase the R&D capacity of organizations
Support joint R&D projects between firms and universities
Support higher education programs
Subsidies for R&D infrastructure
(laboratories, research and technologies centers, research groups, etc.)
Support (financial) for increasing mobility between academia and industry
Support for commercialization of research results | Typical innovation policy:
Support on-the-job learning and organizational innovations
Matchmaking activities and building and sustaining existing networks
Stimulate trust building andjoint innovation projects between actors in the value chain
(producers-suppliers, users-consumers)
Stimulate joint projects between competing and auxiliary businesses |
Table 1.2 Analytic vs. synthetic knowledge bases
| Synthetic knowledge base | Analytic knowledge base |
|
| Innovation by application or novel combination of existing knowledge | Innovation by creation of new knowledge |
| Importance of applied, problem-related knowledge (engineering), often through inductive processes | Importance of scientific knowledge often based on deductive processes and formal models |
| Interactive learning with clients and suppliers | Research collaboration between firms (R&D department) and research organizations |
| Dominance of tacit knowledge due to more concrete know-how, craft and practical skill | Dominance of codified knowledge due to documentation in patents and publications |
| Mainly incremental innovation | More radical innovation |
On the other hand, the synthetic knowledge base refers to “industrial settings, where the innovation takes place mainly through the application of existing knowledge” (Asheim and Gertler, 2005, p. 295) or through new combinations of knowledge. It is the case of incremental innovations, which are developed to solve specific problems as, for example, in the field of industrial machinery or shipbuilding, where products are generally manufactured on a small scale. Research and development (R&D) and university-industry links tend to be less important compared to the analytic knowledge base, and knowledge is often produced as a result of experimenting, testing and practical processes presenting a low level of codification. Main characteristics and differences of the two knowledge bases are summarized in Table 1.2...
