Local Transition and Theories of Innovation Processes
Regional Innovation Systems ’ Approach
The regional innovation systems ’ approach emerged as a response to the shortcomings of neoclassic theory in explaining the spatial clustering of innovation and technological change (Alkemade et al. 2011; Wieczorek and Hekkert 2012; Hekkert et al. 2011). Innovation system analyses aim to explain innovation and economic change with the systems’ structural elements, functions, or phases, and by identifying its possible weaknesses.
A first definition of innovation systems has been given by Freeman (1987), who states that “systems of innovation are networks of institutions, public or private, whose activities and interactions initiate, import, modify, and diffuse new technologies” (p. 1). The innovation system analysis’ perspective tries to explain the “competitive advantage of specific nations, regions, or sectors in terms of the interplay of context-specific actors, technologies and institutional infrastructures” (Coenen et al. 2012, p. 969). This advantage arises in a process of evolving structural conditions, networks of firms, knowledge institutes, and public authorities, together with a strong concentration of financial and human capital, processes of knowledge flows, institutional learning and exchange of context-specific and tacit knowledge (Asheim and Coenen 2006; Bessant et al. 2012; Cooke 2012a, b; Dobusch and SchiBler 2013; Gertler 2003). Innovation system analysis’ analytical advantage is that it deals with the identification of systemic strengths and weaknesses as well as the capacities of the innovation systems’ structural elements that are highly critical to the functioning of the system. More precisely, it combines structural characteristics concerning actors, institutions, and infrastructures with functions, interactions, emerging knowledge flows and exchange of tacit information (Chaminade and Edquist 2010; Jacobsson and Bergek 2011; Klein Woolthuis et al. 2005; Wieczorek and Hekkert 2012).
Following Porter’s (1998) seminal works on geographically close innovation clusters, there is a heap of business studies literature about local context and innovation which support the general ideas of the regional innovation systems’ approach. This literature investigates how geographic concentrations of interconnected firms, and supporting as well as coordinating organizations influence innovativeness and performance of regional economies (e.g. regional differences in wages or (un)employment rates) (Delgado et al. 2010, 2014; Fang 2015; Porter 2003). The main focus of these studies is to analyze the local factors that drive innovation. First, firms and organizations link closely by complementarities and trustworthy relationships by competitive and cooperative interactions (Hamdouch 2007; Porter 1998). Here, a set of institutions define the framework for cooperation, production, and pricing by the means of public or private law (‘hard’ rules), and serve as informal and more tacit codes on how to interact within the economic network (‘soft’ rules) (Hekkert et al. 2011). These institutions are key to the absorptive capacity of the cluster as the “set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability” (Zahra and George 2002, p. 186). Second, other moderators such as sectors, firm size, firm age, cluster centrality or degree of firms’ specialization are found to be influencing the clusters’ innovative performance and geographic concentration (see meta-analysis by Fang 2015). These factors are strongly bound to the resources and infrastructures available to the cluster, such as streets, public transport, buildings, technologies, machines, and financial or human capital but also knowledge, expertise, know-how and strategic information (Asheim and Coenen 2006; Gertler 2003; Hekkert et al. 2011; Lane et al. 2001; Wieczorek and Hekkert 2012; Zahra and George 2002).
The observation that these factors are distributed unevenly across different regions suggests a clustering effect of innovations. It is therefore not surprising that the vast amount of literature focuses on the regional level, especially on cities and urban agglomeration respectively (Delgado et al. 2010, 2014; Fang 2015; Salamonsen and Henriksen 2015). However, recent studies provide a more skeptical view on this perspective. Fang’s (2015) meta-analysis of influencing factors on the innovation performance of economic clusters reveal rather inconsistent results and a high level of variance on the findings from the case studies analyzed concerning the role of geography. Todtling and Trippl (2005) go one step further and distinguish between different localities stating that “innovation activities differ strongly between central, peripheral and old industrial areas”. Shearmur (2015) even points out evidence for innovations to occur in peripheral regions (e.g. Cooke 2011; Fitjar and Rodriguez-Pose 2011; Grillitsch et al. 2015; Knox and Mayer 2009; MacPherson 2008; Petrov 2011; Shearmur 2011, 2012). The author argues that these “isolated places may replace buzz and geographic proximity by various types of social and network proximity, may rely on local knowledge that is difficult to communicate, may be closely connected with local resources, or may innovate in certain areas (environmental sustainability, mining or agriculture, for instance)” (Shearmur 2015, p. 424). This perspective on peripheral innovation is crucial when it comes to analyzing local energy transition processes in rural areas (Balta-Ozkan et al. 2015). Available land for wind-farms, planting energy crops or building solar- parks is a local resource that is bound to the peripheral areas. Local knowledge on farming methods and adaptation of energy technologies to the local context is grounded on social networks other than usually persistent in urban clusters. Therefore, the regional innovation systems’ approach serves as a good framework to analyze the basic infrastructures, networks, interactions and functions of a local innovation system. However, in order to identify the relevant actors, networks, interactions, institutions and infrastructures that are key to the local transition processes we draw insight from the extensive literature of the sustainable communities approach.