One way to describe policy models which are based on triple or quadruple helix theory and their connectivity, is through the idea of connected or disconnected region. Virkkala (2014: 30) has described a connected region as a “norm or vision according to which the actors of different helices are working in the same direction
and linked to economic development and innovation.” Different helices are expected to work together and reinforce each other (Virkkala 2014: 30; Goddard, Kempton & Vallance 2013; Goddard & Kempton 2011). In a connected region, the three helices coevolve and interact through various networks and organisations (Dolfsma & Leydesdorff 2009).
According to Virkkala (2014: 30), in a disconnected region: “…the partnerships are ineffective or non-existent, and there is a lack of understanding about the changes. Entrepreneurs are locked out of regional planning.” There are also no boundary spanners (Virkkala, Mäenpää & Mariussen 2017: 665; Goddard, Kempton & Vallance 2013). The presumption is that greater connectivity enhances innovation capabilities. For example, Amin and Thrift (1995; Rodríguez-Pose & Wilkie 2017: 39) have highlighted this idea by stating that “institutional thickness” is very useful for efficient innovation activities. Connected region has been described as a vision or target that the region should aim to achieve (Virkkala, Mäenpää & Mariussen 2017; Virkkala 2014: 30).
However, the relation between connectivity and innovation is not always clear. Firstly, regions operate on different geographical levels and are open in nature (Virkkala 2014). Similar thinking is in the RIS3 guide (Foray et al. 2012), that points out the importance of local embeddedness and relatedness. Foray et al. (2012: 15) warn that “by concentrating only on embeddedness, a regional development strategy may risk increasing vulnerability to changing economic conditions.” Therefore, it is important to focus on the relatedness as well, which helps in diversifying regional stakeholders into related areas based on innovative techniques or processes (Foray et al. 2012: 15). In this study, relatedness refers to extra-regional connections, and the embeddedness of regional connections. Secondly, helices should not dominate other helices (Virkkala 2014: 30). Qvortrup (2006) points out that different helices need to have their own roles and rules, but there should be more interaction between them. This means that helices should be separate from one another and yet closely interlinked (Qvortrup 2006). In a connected region there should be different helices, which operate in harmony. They should produce services that the other helices cannot arrange. For example, public institutions create the rules and regulations and thus provide general conditions for both companies and research institutions. (Virkkala 2014: 31; Qvortrup 2006.)
Thirdly, the causality between connectivity and regional innovativeness is not in itself clear. Boschma and Frenken (2013) have discovered an aspect known as the proximity paradox. If the cognitive proximity is low, then their collaboration might not increase innovation performance; on the contrary, it might give rise to lock-
ins. An intermediate level of differences in knowledge bases is needed for innovative cooperation. Another issue relates to the idea that strong ties are preferable, which is not necessarily true. According to Granovetter (1973) weak ties are important since they can connect different social groups and serve as bridges. Therefore, one cannot state that strong connections to everywhere is the solution, but there must be an optimal balance of socially proximate and socially distant relations. (Virkkala, Mäenpää & Mariussen 2017.)
This is why the potential of a relation depends on optimal levels of proximity, and on a balance between local and non-local ties (Virkkala, Mäenpää & Mariussen 2017: 677). An innovative region should be locally embedded, but at the same time oriented towards global knowledge and wider markets (Virkkala, Mäenpää & Mariussen 2017: 666).
Relations between different actors can be described through different dimensions of proximity, including geographical, organisational, social, cognitive and institutional proximity (Boschma 2005). Proximity is mandatory in some dimensions, where it connects actors and enables interactive learning and innovation. However, this may not always be the case (Virkkala 2014: 57). Harmaakorpi, Melkas and Uotila (2017) have demonstrated this regarding broad- based innovation policies, and they have formulated them into three different categories based on various differences regarding, for example, economic logics, knowledge bases (based on Asheim & Coenen 2005) and fuel for innovation. This latter category is especially interesting as it suggests that the role of proximity (especially cognitive proximity) can vary in different types of innovation policies. The categorisation of Harmaakorpi, Melkas and Uotila (2017) is summarised in Table 2.
However, if one inspects the categorisation made by Harmaakorpi, Melkas and Uotila (2017) more closely, it is possible to think also of other proximities (such as social proximity and institutional proximity) which may affect the logic of different innovation policy modes. For example, the first mode, based on agglomeration, could also mean high geographical, social and institutional proximity besides the cognitive proximity. On the other hand, the second mode (2a) might require a certain cognitive distance but might also benefit from geographical and especially social proximity in order to establish knowledge fertilisation. The third mode (2b) might require social and institutional proximity besides cognitive proximity. Harmaakorpi, Melkas & Uotila (2017) have highlighted the role of cognitive proximity especially and even though Virkkala (2019; Virkkala, Mäenpää & Mariussen 2017) agrees with the importance of cognitive proximity, she claims (Virkkala 2019: 168) that other dimensions of proximity may contribute, to some
extent, to the lack of cognitive proximity: “…some degree of cognitive proximity is needed so that people can learn from each other and collaborate successfully, and other dimensions of proximity, such as the social, institutional, and organizational forms may facilitate that”.
Table 2. Innovation policy categorisation (based on Harmaakorpi, Melkas
& Uotila 2017). Innovation policy categories Logic for knowledge generation Theoretical
basis Innovation process requirements
Innovation
outcomes Role of proximity
Science-based innovation (STI, Mode 1) Scientific knowledge production at a high level, in a very narrow field Agglomeration – Economies of scale Analytical knowledge base Critical mass of experts Scientific knowledge and technical innovations Proximity (especially cognitive) Practice-based innovation (DUI, Mode 2a)
Intellectual cross- fertilisation; knowledge from different knowledge bases Innovation platforms – related variety Synthetic knowledge base Systemic process, where scientific and practical expertise are combined Products, technological system innovations Distance (especially cognitive) Practice-based innovation (DUI, Mode 2b) Heterogeneous long-term development of organisations Value networks – dynamic Capabilities Symbolic knowledge base Learning by doing in communities of practice Organisational, social and service innovations Near distance
According to Virkkala (2014: 36) different types of proximity can explain the formation of networks, as they may overlap and there also can be an interplay between them. Furthermore, different dimensions of proximity may act as substitutes rather than complementary in innovation networks. Proximity is required in at least one dimension to form a successful relation. (Virkkala 2014: 36.)
Indeed, Ponds, van Oort and Frenken (2009, according to Boschma 2009) have discovered that geographical proximity is particularly required during the establishment of triple helix relationships (where institutional proximity is low)
and less important in collaboration among organisations with similar institutional backgrounds (where the institutional proximity is high). This discovery seems to verify that different aspects of proximity are important for different types of innovation systems and can indeed act as substitutes.
High proximity can be considered to be mandatory for forging connections between stakeholders, regardless of its nature (Virkkala 2014: 36). However, proximity between stakeholders may sometimes even harm the innovative performance (Virkkala, Mäenpää & Mariussen 2017: 666). According to Boschma and Frenken (2009), the level of proximity between agents has an effect on their innovative performance. Success of a relation depends on optimal levels of geographical, social, institutional, organisational and cognitive proximity as well as on a balance between regional and extra-regional links (see Table 3). This may also mean that an optimal level requires operating simultaneously in different institutional systems, especially in triple helix setting (Virkkala 2014: 37). This means that institutional proximity needs to be balanced regarding all three helices; as high institutional proximity in one might mean that the two others are neglected. Virkkala (2014: 31) has described this with an example: “If one makes the research system too business minded, then one prevents it from generating new knowledge. If one places too many restrictions on companies, then one reduces their production of goods and services. If one makes public institutions effective, then they might find it difficult to meet their duty to provide wide public welfare.”
Table 3. Different dimensions of proximity in the relations of a triple
helix framework (Virkkala 2014: 36).
Dimension of proximity Degree of proximity
High Low
Geographical Relations between actors in the region Relations between actors in the region and abroad Institutional (helices)
Relations between firms Relations between universities Relations between public organisations
Relations between actors in different helices
Cognitive (knowledge
base) Similar knowledge base of actors, actors in the same cluster Different knowledge bases of the actors
Social Relationships based on friendship and reciprocity Formal relationships Organisational (type of
network)
Relationship between one type of network, between units of a global firm or the same public sector (such as the environment)
The proximity concept can be used analytically in the triple helix context, as has been demonstrated by Virkkala (2014: 36) in the Table 3. In this case, a relation acts as an indicator for close proximity between partners regarding at least one aspect of proximity. Stakeholder has expectations regarding cooperation if his or her partner is close enough in at least one proximity dimension. (Virkkala 2014: 37.) The strength of the relationship depends on the figures for expectations and experiences. Furthermore, the quality of the relationship can be measured in the gap between the expectations and experiences (Virkkala, Mäenpää & Mariussen 2017). If the gap is high, then expectations have not been met.