Research orientation and agglomeration: Can every region become a Silicon Valley?
Introduction
Success stories, such as the tale of ever-vibrant Silicon Valley (, ), appeal to practitioners and scientists alike, because these examples allow us to envision desired successes. Despite the growing interest of policy makers and scientists in regional learning and innovation, an increasing ambiguity exists in the evidence base (Morgan, 2004). Our understanding of the processes of innovation and learning at the regional level still has many blind spots. At the core of this ambiguity and lack of knowledge is our inherent favouring of success stories, thereby neglecting lessons to be learned from less successful but possibly more relevant endeavours.
During the early 2000s, policy for regional innovation systems stimulated linkages between university and industry as well as ‘institutional thickness’ (, ). However, although important, neither can substitute for a strong local corporate sector or a strong scientific system (, ). Therefore, there is no “ideal model” of policy regarding regional innovation (Tödtling and Trippl, 2005). By creating suitable conditions policy interventions can induce further development of regions to some extent. However, they never suffice to initiate or sustain innovation and technological change in regional innovation systems.
Recent innovation policy has been criticized for its tendency towards ‘copy-and-paste’ policy following successful examples of innovative regions, regardless of its fit with the specific regional innovation system at hand (e.g. Boschma, 2004; Hospers and Beugelsdijk, 2002; Tödtling and Trippl, 2005). There are a few examples of more sophisticated forms of regional benchmarking. Those benchmarks study various types of regional innovation systems and can be useful tools for regional policy makers (Huggins, 2010). Particularly, studying less successful regions – i.e. dysfunctional or failing regional innovation systems – would contribute to understanding regional innovation systems (Asheim et al., 2011a), and to improving regional benchmarking practices. Moreover, while policy and academic interest are often directed towards high-tech sectors, innovation policy should also stimulate regions endowed with low and medium tech, i.e. more traditional, industries (Tödtling et al., 2009).
Regional innovation systems with different contextual characteristics have different structural and functional requirements as research processes driving them are path-dependent (Malmberg and Maskell, 1997). Instead of continuously analysing success stories, such as the Silicon Valley, it is crucial to recognize different contexts of regions and to also analyse non-success stories. To this end, a framework distinguishing three distinct types of regions, i.e. peripheral regions, old industrial regions, and metropolitan regions, provides insights into problems and suggests suitable interventions associated with failures in innovation systems (Tödtling and Trippl, 2005). The framework has been applied to case studies of specific industries or regions (e.g. Isaksen and Karlsen, 2013; Tödtling et al., 2011; Trippl and Otto, 2009) and is an important complement of the success-oriented stories in the literature, because it focuses on the non-success regions.
Following the suggestions of a number of recent influential papers (e.g. Arencegui et al., 2012; Bergek et al., 2008; Edquist, 2011), in this paper, we compare success and non-success regions. To do so we make use of two mechanisms that capture sources and evolution of success within regional innovation systems, i.e. research orientation and agglomeration patterns. In essence, regional learning and innovation are organic and self-activating processes based on local circumstances and development paths (Morgan, 2004). Regional research orientation constitutes local circumstances of regional innovation processes. At the same time agglomeration patterns point to the development paths. Both research orientation and agglomeration patterns are given in the short run but can be adapted in the middle and long run, thereby lending themselves for policy measures. Although there have been suggestions that agglomeration and research orientation may interact and affect economic success of regions (e.g. Feldman, 1994; Varga et al., 2012), our study connects the two concepts and studies their co-evolution.
Knowledge generation and diffusion affects many structural and functional elements in the regional innovation system, e.g. innovation activities by regional firms and other stakeholders such as academia and governmental agencies (Asheim et al., 2011a). For knowledge to diffuse, interactions between innovative agents in different subsystems of regional innovation systems are necessary. While innovative agents generate and diffuse specific types of knowledge they influence regional innovation systems’ research orientation, i.e. the quest for fundamental understanding or attention for considerations of use (for details see Section 2.2). Agglomeration patterns materialize as a result of dynamic externalities, which have been identified as the source of innovation and economic growth (e.g. Glaeser et al., 1992; Jacobs, 1969; Romer, 1986). There are two kinds of agglomeration patterns, namely: MAR and Jacobs’ externalities (for details see Section 2.3). While MAR externalities emerge from knowledge spillovers between innovative agents belonging to specialised and related industries (e.g. Glaeser et al., 1992), Jacobs’ externalities result from knowledge spillovers between innovative agents belonging to various industries (Jacobs, 1969).
The paper is organized as follows: We start by introducing the idea of context-specific policy as well as investigating the co-evolution between research orientation and agglomeration patterns (Section 2). Subsequently, we introduce the data on thirty-six European Union (EU) regions we use as well as our research design and analysis (Section 3). After having typified all regions according to research orientation, agglomeration pattern and a number of basic economic indicators, such as regional GDP per capita and unemployment rates (Section 3.3) we analyse the success and non-success regions (Section 4.1). Our findings lead to theoretical propositions and we provide a revised version of Stokes (1997) quadrants with which one can theoretically assess regions (Section 4.2) as well as draft context-sensitive policy (Section 4.3). We conclude with a brief summary of our contribution to theory and practice and add suggestions for further research (Section 5).
Section snippets
Context-sensitive policy making for regional innovation systems
Innovation has drawn academic and societal interest ever since works of , spurred its introduction. Traditionally, a linear perspective of the innovation process dominated the innovation studies literature, implying innovation to follow distinct stages starting at research and leading to eventual commercialization, without any feedback between those stages (Edquist and Hommen, 1999). This step-by-step thinking proved to oversimplify and misrepresent real-life innovation. Rather, innovation
Thirty-six regions from EU-15 countries: Analysing the data
Regional innovation systems combining a specific type of research orientation with a particular agglomeration pattern differ in structural and functional requirements. For example, a region where market-oriented research is prevalent, is likely to benefit from regional agglomeration, whilst a region with more science-driven research is likely to profit from research networking across regional boundaries (Varga et al., 2012). In the following, we will empirically analyse research orientation and
Success and non-success stories
Specific combinations of research orientation and agglomeration associate with different levels of performance of regions (see Section 2). Here, we integrate the qualitative results on research orientation and agglomeration with the quantitative results on performance. In Table 2 you find the basic economic profile for all regions. Fig. 3 visualizes the results in one conceptual matrix (see Appendix B for a table integrating the results displayed in Fig. 3).
MAR-type regions perform relatively
Conclusions
In our analysis we go beyond either analysing successful regions (such as Silicon Valley in , ) or failing regions (such as in Tödtling and Trippl, 2005). Based on our comparison of thirty-six regions we could identify regional patterns that are associated with success or non-success of regional development. Our findings do not only inform context-specific policy but also offer an interesting new theoretical perspective. Returning to the question in the title of our paper, whether every region
Acknowledgements
Earlier versions of this paper were presented at various academic conferences. The authors wish to thank all those who provided insightful feedback for our work at those occasions. We also thank the anonymous reviewers of the Technovation journal for their helpful suggestions. The European Commission's Directorate-General for Enterprise and Industry and the Regional Innovation Monitor project are acknowledged for making available the primary studies used for this paper. Claudia Werker partly
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