Regional Innovation Support Systems and Technopoles - Technopolis: Best Practices for Science and Technology Cities - page 47

each other. Firms are therefore institutionally embedded in different contexts for
interactive learning. Spatial proximity stimulates communicative interaction
between actors. However, it is not a sufficient condition. In order to achieve this
interaction social proximity (equal or similar characteristics such as age, vocation,
language and equal or similar views on values and norms) and organisational
proximity (concern structure, intra- and inter-firm network structures) are neces-
sary factors as well (Boschma 2005 ). The knowledge form determines to what
extent proximity is necessary for learning by interacting. Typically, innovation-
relevant information is not a publicly available, codified good, but private tacit
knowledge—those parts of personal knowledge as well as personal skills that
cannot be communicated in an impersonal way. Only through personal, commu-
nicative interaction between actors there are possibilities to exchange, understand
and to apply this kind of information. In order to communicate, tacit, and to a
lesser extent codified knowledge 'code keys' are needed, which are only under-
standable if (social) coherence and proximity are available. Thus, institutional
embeddedness in regions positively affects the communication of tacit knowledge
in particular and learning by interacting in general, which in turn is positive for
competitiveness. Collective learning processes and a collective tacit knowledge
are linked to the location because of the coinciding of social, cultural and spatial
proximity (Boschma 2005 ). At the same time, however, Bathelt ( 2003 , 772)
stressed that one should not forget the role of the non-local for competitiveness:
''In addition to mobilising internal resources, regional policies should also support
agents in developing linkages and networks with external agents and markets …
Caution should … be exercised in prioritising the local capabilities over non-local
opportunities''.
Since regional innovation policies have been emerging starting in the mid-
1980s, several academics have started to develop theoretical and conceptual ideas
on regional innovation strategies since the mid-1990s (Moulaert and Sekia 2003 ).
These concepts, which form an important part of the so-called family of territorial
innovation models, that is regional innovation systems (Cooke 2004 ; Mothe and
Paquet 1998 ; Asheim et al. 2011 ), the learning region (Morgan 1997 ) and clusters
(Porter 2000 ; OECD 2007 ) have been partly developed for policy reasons, namely
as a response to organisational and strategic weaknesses of regions. Scholars also
wanted to derive conceptual policy lessons from successful regional economies
and to clarify why the regional level is an important level as a source for learning
and innovation.
Of the developed concepts, the regional innovation systems concept is most
widely dealt with in direct combination with regional innovation policy, both in a
conceptual way (Cooke 2004 ; Mothe and Paquet 1998 ) and concerning empirical
case-studies, including North American and Asian ones (Cooke et al. 2004 ; Mothe
and Paquet 1998 ). The learning region, on the other hand, clearly lost its impor-
tance (Hassink and Klaerding 2012 ). Cooke et al. ( 1998 :1581) define regional
innovation systems as systems ''in which firms and other organisations [such as
research institutes, universities, innovation support agencies, chambers of com-
merce, banks, government departments] are systematically engaged in interactive