Special issue: clustering and innovation: firm-level strategizing and policy

Introduction

In the last two decades, the geography of productive activity has become a crucial aspect in various fields of economic research. This resurgence of territory as a fundamental economic factor is a consequence of the growing awareness of the fact that regional variations in terms of economic growth and performance ultimately depend on a set of relatively immobile resources – knowledge, skills and institutional and organizational structures – whose role is now recognized as highly important (Breschi and Malerba 2001). A growing number of scholars of the industrial and innovation economies devote their efforts to analysing the geographical dimension of innovative activities through a range of conceptual and empirical analyses, with relevant contributions from not only economics but also sociology and business.

The study of clusters is particularly important. Interest in clusters owes to their spatial and social proximity. For example, high growth firms have huge innovation potential, which depends heavily on the competitive environment (Brown, Mawson, and Mason 2017). The geography of high-growth firms, which profoundly affects regional development, seems to follow a spatial logic (Li et al. 2016).

The term cluster expresses the existence of a geographical concentration of similar economic activities in terms of inputs, technology or products and markets. This term is general. Other concepts are used to describe more specific forms of clusters: growth poles, industrial districts, regional innovation systems, innovative milieus, industrial complexes, learning regions, local production systems and regional clusters, to name but a few. Although they are not synonymous, they all refer to spatial concentrations of economic activity. Consistent with the vision that innovation drives economic growth, they emphasize the importance of knowledge for regional competitiveness (Morgan 1997; Torre 2008; Vissers and Dankbaar 2016).

The literature identifies three types of groupings of local firms that should be distinguished from clusters (Gordon and McCann 2000; Granovetter 1985; Simmie 2004). The first is the agglomeration. More than cooperation, this relates to discontinuous local interactions resulting from the presence of a large number of other firms and ecological processes of natural selection among firms. The second is the industrial complex, in which the determinant factor of concentration is the reduction of transportation costs resulting from the location of natural resources. The third type is based on social networks. In this case, relationships of trust make firms willing to undertake joint initiatives and sometimes act together to achieve common goals.

As Isaksen (2016) explains, the emergence of clusters requires the right conditions for groups to emerge, as well as triggers that determine that clusters emerge in particular locations. To analyse the emergence and effects of concentrations of firms, the regional literature has used different theoretical approaches. Notable contributions include that of Becattini (1979) on the Marshallian industrial district and the externalities or network approaches (Camagni 1993). More specifically, different approaches have been used to investigate the reasons that justify the superior innovation performance of firms located in clusters and the complex, heterogeneous territorial distribution of innovation competencies. Notable approaches include those used in the case studies of technological clusters (Saxenian 1994), the analysis of technological districts and new industrial and technological spaces (Storper 1992) and the study of innovative media (Capello 1999; Maillat, Quévit, and Senn 1993). In addition to these contributions, it is worth highlighting the importance of both the theoretical approach and policymakers in shaping the approach to regional innovation systems (Cooke 2001; Cooke, Uranga, and Etxebarria 1997; Isaksen, Martin, and Trippl 2018; McAdam and Debackere 2018).

There are essentially two sources of benefits of geographical groupings of companies. First, it helps new business projects emerge by lowering barriers to entry. The inputs and resources (human, technical and financial) that are necessary for business activity are located in a certain environment, so new entrepreneurial projects are easier to access. Second, clusters promote cost and productivity advantages for localized businesses. The interrelation between such firms enables the search for complementarity between activities. Also, institutions, inputs, specialized services and the like can be accessed more flexibly and cheaply.

Innovative firms can take advantage of belonging to a cluster because the frequent, recurring connections established between different agents help detect new technological opportunities, identify demand for innovation, improve access to knowledge-intensive services and so on. Several authors have stressed the importance of learning processes, networks and inter-firm cooperation in cluster performance (Harhoff, Henkel, and von Hippel 2003; Saxenian 1991). The stimulus for innovation is derived from certain characteristics of the cluster, especially in relation to the structure and combination of knowledge flows (Audretsch and Feldman 2004; Cooke 2001; Crass, Rammer, and Aschhoff 2016). Information and communication technology has led to a decrease in the cost of transferring information over long distances. However, the cost of transferring tacit knowledge increases with distance to the extent that this transfer requires trust, direct relationships and a common culture. Innovations that generate greater competitive advantages involve a greater component of tacit knowledge (Mudambi and Swift 2012; Zucker, Darby, and Brewer 1998), and the flows of this type of knowledge are encouraged by geographical proximity. Among other reasons, this is because the transaction costs of generating trust between different components of the transmission of knowledge are reduced (Maskell 2001). Hence, for the stimulation of innovation, different authors engage in forms of knowledge exchange based on informal networks that are built on trust and reciprocal understanding (Maskell 2001; Tallman et al. 2004) or relationships between university and industry (Audretsch and Stephan 1996).

Several studies have shown the existence of positive effects of clusters (Czarnitzki and Hottenrott 2009; Zucker, Darby, and Brewer 1998). Other studies are more nuanced, indicating that grouping by itself does not lead to better innovation performance. According to the latter group of studies, certain conditions are required for the geographical grouping of firms to provide a real stimulus for innovation. Thus, some studies indicate that the greatest positive impact of the effects of a cluster is felt by the youngest firms and those with greater stocks of knowledge (McCann and Folta 2011). Kukalis (2010) underlines the importance for groups of firms to have similar degrees of absorptive capacity and innovation potential to benefit from the advantages of groupings. Beaudry and Breschi (2003) conclude that although belonging to a group that is densely populated by other innovative firms positively affects the likelihood of innovation, the disadvantages for non-innovative firms in the same industry seem considerable.

Contributions to this special issue

This special issue of Entrepreneurship and Regional Development brings together nine papers. Adopting diverse perspectives and a range of methods, these papers make interesting contributions to the topic of clustering and innovation.

In ‘Development of a multidimensional measure for assessing entrepreneurial ecosystems’, Liguori, Bendickson, Solomon and McDowell develop an index to determine a region’s entrepreneurship friendliness. Clusters of innovative firms emerge under certain conditions, so geographical aspects of local ecosystems play a key role in the success or failure of these firms. Accordingly, this study aims to better define and identify entrepreneurship ecosystems through an entrepreneurial ecosystem index (EEI). This goal is accomplished by assessing the six non-causal critical domains of entrepreneurship (policy, finance, culture, support, human capital and markets) through the perception of individuals within the given ecosystem. The novelty of the EEI lies in the assessment of ecosystems at any level (city, state, region, etc.). The main contributions of this research are (1) to show that entrepreneurship ecosystems can be measured through perceptual techniques at many levels and (2) to provide practical value through this tool not only in an academic setting but also in terms of policymaking by offering deeper insight into the fundamental dynamics that drive the success of a community ecosystem.

In ‘Developing relationships in innovation clusters’, Scott, Hughes and Kraus assess the social conditions that facilitate (or obstruct) knowledge diffusion within innovation clusters. It is vital to understand how core relationships engage within wider networks of activity to become familiar with the determinants of sustained innovation and the potential effectiveness of clusters. In clusters, members participate when it is apparent that there is a win-win effect at play and when effective clusters improve firms’ access to information, knowledge, resources and institutions. The objectives of this study are to clarify the determinants of cluster effectiveness by providing an in-depth examination of the impact that various network structures have on the performance, management and outcomes of university–business relationships, specifically those built for new product development and complex knowledge transfer. Universities have long been acknowledged as regionally embedded resources for innovation activity within clusters by both policymakers and scholars, who provide a particularly potent form of open innovation. The most important result of this study is to show the process of creating new knowledge and the significant contribution to the innovation process within the network through collaborative work. Furthermore, as universities connect and their networks increase in complexity, multiple network levels must be coordinated simultaneously to ensure that all links are managed appropriately.

In ‘Enterprise level cluster innovation with policy design’, Xu, Xiao and Rahman investigate external policy design for cluster innovation by examining China’s current transition from a centrally planned to a market-based economy. The contribution of this paper is not only to describe the time process of micro to small enterprise clustering but also to provide policy design ideas on how to achieve rapid micro to small enterprise clustering. During this transition period, Chinese government policy implementation may help or serve to provide support to the high level of the output value of the industrial cluster. The study focuses on the effectiveness of policy design, which is explained by the interval change in output value and percentage/proportional change. To enhance the national economy, the government’s industrial policies should emphasize co-operation between governments, private firms, banks and workers. The conclusions of this paper are (1) that the design of government policy will help clustering from micro-enterprises to small enterprises and (2) that enterprise structure and tax benefits play prominent roles in industrial clustering, whereas providing loans to enterprises has a minor impact.

In ‘Disruptive technology adoption, particularities of clustered firms’, Molina, Martínez and Valiente study the extent to which the internal attributes of a clustered firm influence its capacity to adopt a disruptive innovation. The absorptive capacity model is studied through the potential (exploring) and realized (exploiting) domains, consequently distinguishing between the four dimensions of absorptive capacity: acquisition, assimilation, transformation and exploitation. The objective is to find the firm-specific attributes that allow individual clustered firms to access and exploit a new disruptive technology more rapidly. A four-dimensional perspective is interesting because each dimension requires distinct organizational processes and develops differentially in clusters. Evidence for these causal relations is obtained from empirical analysis of firms in the Spanish ceramic tile cluster that have widely adopted disruptive innovations. The most important finding is that potential absorptive capacity does not seem to have a clear effect on the early adoption of technological novelty by clustered firms. This finding highlights the uneven effect of the potential (exploring) and realized (exploiting) domains of the absorptive capacity model in the early adoption of disruptive technologies by clustered firms. Network idiosyncrasies and proximity in these kinds of regional organizations may be behind this result.

‘Innovation performance and the role of clustering at the local enterprise level’, by Pickernell, Jones and Beynon, offers a novel configurational approach to investigate innovation performance and clustering in English Local Economic Partnerships (LEPs). The study focuses on the integration of cluster analysis with the contribution of a range of actors in SME innovation creation and dissemination frameworks. The authors analyse evidence from English Local Enterprise Partnerships (LEPs), introduced by the Conservative-Liberal Democrat coalition government in June 2010. Particularly for policymakers in Europe, pressure has been growing to develop effective policies promoting cluster development to increase benefits for the regions and nations where clusters are situated. The results indicate that the existence or non-existence of clustering in high and low innovation outcomes is important and that, to lead to these outcomes, other variables are also relevant, most obviously local firm innovation itself.

‘Industrial clusters, flagship enterprises and regional innovation’, by Anokhin, Wincent, Parida, Chistyakova and Oghazi, explores the effect of flagship enterprises and concentrated industrial clusters on regional innovation. The authors posit that regional educational attainment positively moderates the effect of industrial clusters on innovation. At the same time, flagship enterprises primarily affect regional innovation in regions with low education levels. The authors suggest that the impact of clusters on local innovation outputs would be most visible when prevalence of higher education is high, whereas the innovative contribution of flagship enterprises would be most visible in areas where local conditions are unlikely to facilitate innovative breakthroughs. This means that their innovative impact will be most apparent where local levels of education are lower. The main findings are (1) flagship enterprises stand to benefit little from the locally available qualified workforce because they are not truly reliant on local resources and (2) industrial clusters do their best at bringing new ideas to the forefront in regions that boast above-average availability of college graduates. Moreover, encouraging the in-migration (or creation) and discouraging the out-migration of flagship enterprises is important to ensure local employment and promote innovation.

Kusa, Marques and Navarrete examine the relationship between external cooperation and entrepreneurial orientation in ‘External cooperation and entrepreneurial orientation in industrial clusters’. Entrepreneurship and external cooperation between organizations significantly contribute to value creation by firms and the economy as a whole. For example, collaborative entrepreneurship reflects a company’s ability to collaborate outside the organization. The study’s main contribution is to confirm the positive correlation between external cooperation and entrepreneurial orientation. This finding indicates that collaboration with external entities can support the pursuit of entrepreneurial opportunities. This conclusion does not refute the role of competitive attitudes, but it does suggest that competitive behaviour is not the only attitude that entrepreneurs can take towards competitors. The findings also show that trust, cooperation with other organizations and cooperation with customers are positively correlated with firm performance. Cooperation with customers is strongly correlated with entrepreneurial orientation. These findings indicate that one of the basic characteristics of entrepreneurial behaviour is openness to customer needs, which reflect potential entrepreneurial opportunities. Thus, to analyse and interpret entrepreneurs’ relationships with competitors, the concept of coopetition (the combination of cooperation and competition) may be more relevant than the concept of competition.

In ‘Accelerators as start-up infrastructure for entrepreneurial clusters’, Bliemel, de Klerk, Flores and Miles explore the role of accelerators, supporting organizations and start-ups in Australia’s tech start-up clusters. They suggest that the infrastructure of an entrepreneurial cluster is the portfolio of public goods that supports and facilitates entrepreneurship. The components of a cluster’s infrastructure depend upon the specific objectives of the cluster. This study takes a closer look at the infrastructure of an entrepreneurial cluster by analysing accelerators as infrastructure for start-ups. Accelerators facilitate the creation and growth aspirations of start-ups by bundling support services that typically include mentorship, educational programmes, networking and equity-based seed funding. Initially, accelerators were private sector operations. Now, however, many accelerators receive public funding. The study yields two main findings. First, accelerators provide infrastructure for start-ups in entrepreneurial clusters. The accelerator infrastructure is dependent on the cluster it is related to and the community capitals it can assess. Therefore, creating and operating an accelerator is complex because of the interdependence with the infrastructure and cluster’s community capital. Second, accelerators facilitate knowledge overflow and create community capital through mentors who continually reinforce linkages between the accelerator and the cluster.

In ‘Public cluster policy and firm performance: Evaluating spillover effects across industries’, Audretsch, Lehmann, Menter and Seitz empirically analyse the spillover effects of public cluster policy in Germany (the so-called Leading-Edge Cluster Competition) on firms and industries that have not been the primary target of the cluster policy but that are located in the same region. The authors investigate path differences between subsidized and non-subsidized cluster regions, confirming that firms in non-subsidized regions that have co-located to subsidized cluster regions face a competitive disadvantage in terms of human, financial and social capital. The main findings suggest that public cluster policy seems to have a negative effect on firms that are not primarily related to the targeted industries. Thus, the concept of ‘agglomeration shadows’ might also apply to industries and related firms.

Conclusions

Researching clusters and innovation has been a key item on the academic agenda in recent years. Interest in clusters stems from the fact that spatial clusters of firms, related suppliers and service industries lead to changes in firms’ competitive environment. Scholars of the industrial and innovation economies are concerned with analysing the geographical dimension of innovation using a range of conceptual and empirical analyses. Relevant contributions have been made in areas such as economics, business and sociology. This special issue contributes to research on clusters and innovation, encouraging research on this topic and enriching our scientific knowledge of this area.

Disclosure statement

No potential conflict of interest was reported by the authors.