5.1.1. Clusters: a reality more than a concept

Our approach aims to analyze clusters as existing organizations within regional economies. The arguments presented are not specific to the European economy. Moreover, they draw on different disciplines (geographic economics, the economics of knowledge and innovation, institutional economics, etc.), which come together to highlight the advantages of proximity. A text published by the European Commission groups these advantages into three categories:

• – “advantages based on regional external economies of scale and agglomeration. These include enhanced productivity arising from localization of production systems, which generates opportunities for greater specialization, division of labor and inter-firm linkages. Agglomeration also provides a basis for enhanced local skills supply and a pool of localized knowledge that is shared between firms;
• – advantages associated with social networks. These are linked to the significance of interpersonal relationships in generating trust within business networks, which is believed to create a social capital that transcends the boundaries between firms and institutions;
• – advantages derived from regional innovation systems and local knowledge exchange. These ideas emphasize the significance of local learning processes that include access to local tacit knowledge and its value in generating competitive advantage” [COM 13, p. 14].

This analysis considers clusters in terms of their concrete form, that is, “as phenomena in the economic landscape of regions” [COM 13, p. 13], highlighting their role as agents of change, which results in increased productivity and a consequent increase in salaries. The data collected by the European Cluster Observatory for a sample of 2,000 clusters confirm the existence of a positive correlation between the presence of clusters¹ and an increase in salaries within the region [KET 15]. The author takes account of the quality of the economic environment: the cluster effect on salaries is stronger in regions with a favorable economic environment. It is important to note, too, that a clear statement of the advantages involved also demonstrates the existence of the geographic concentration processes, which have the potential to affect business competitiveness. The explicit aim of these clusters, according to the European Commission, is to improve the performance of member companies. This is shown in Table 5.1.

This table raises a number of points. The emergence of clusters is principally explained by the existence of localized externalities (Marshallian externalities), which lead to increasing returns to scale. However, the dynamics of externalities and agglomeration economies will only emerge if a significant number of companies are present in the location. We also see that the existence of these clusters is a consequence of multiple successful entrepreneurial initiatives [DAH 06]. Local creation of new companies plays a vital role in cluster growth. The entrepreneurs in question come from within established companies (see Klepper’s analysis, cited above). More specifically, successful entrepreneurial initiatives depend on the number of existing firms and on inputs into the cluster. New companies spring up in the vicinity of those which “created” the entrepreneurs in question.

The dynamic presented in the table does not necessarily contribute to regional or national growth. We see that high regional levels of GDP per capita are not dependent on the presence of individual clusters, but on the number of clusters present within a region that have the potential to reach a critical mass (in terms of the number of companies, volume of employment, etc.).

The influence of social characteristics on performance in terms of productivity is widely recognized. The text published by the European Commission discusses the formation of social networks and social capital, which fosters innovation and entrepreneurship within a local environment, in detail alongside the contents of the table. The effect of social relationships strengthens the agglomeration effect and reinforces the advantages of proximity. The same is true of institutions that do not appear, as such, in the table. However, the accompanying developments refer explicitly to the notion of the “Triple Helix” and to the structuring effects of triangular relationships (between universities, businesses and public actors) in cluster growth, notably for knowledge-intensive structures. These relationships have a favorable knock-on effect on the innovation process, breaking down the walls between different participants.

The EC report also stresses the fact that the evolution of these forms of organization forms part of a lifecycle (derived from an analysis of product lifecycles), which is split into four stages: emergence, growth, maturity and decline. These phases are associated with modifications that affect knowledge intensity, the density of inter-firm relationships and the beneficial effects of localization. In this respect, the analysis differs from that set out in Chapter 1, which took an explicit account of legitimization processes and the quality of social relationships in defining sustainability thresholds and the phase of decline.

In addition to this analysis, it would be interesting to consider the resilience of these organizational forms during periods of crisis. Research carried out on a sample of French companies involved in export activities [MAR 13] indicated that exporting companies belonging to a cluster were more likely to continue exporting than other firms. However, this advantage was considerably lower in 2008–2009: the situation is highly dependent on the position and performance of the leading firm. This shows that the network structure, focused on a central point, has a significant influence on results.

Instead of completely abandoning the theoretical approach and considering clusters as observable “phenomena” [COM 13], it would also be interesting to adopt a realistic position, taking account of other arguments for improving understanding and increasing the competitiveness of clusters [KET 12]. Innovation ecosystems are groups of actors, interconnected by various links. This organization centers on companies that generate innovations and are subject to competition. Research centers and universities have a part to play in this structure via the production of new knowledge, which is often fundamental in nature. Educational establishments reinforce the quality of human capital, and funding bodies (business angels, venture capital funds, banks etc.) have the capacity to validate company projects. Finally, public authorities implement decisions concerning investment in infrastructure, education and so on, which govern the innovation process. In this context, it is necessary, but not sufficient, for clusters to attain a critical mass (as shown in Table 5.1). The intensity of relationships between actors and the possibilities that they create in terms of mobility of both resources and skills strongly affects the innovation pathway and the development of competitive advantages.

Clearly, as the authors note, this chapter describes an ideal situation that differs from reality, as the dialog between actors is often imperfect. Relationships between small to medium-sized firms and big businesses, in particular, can take a long time to establish and even longer to result in effective decisions. Big businesses are often more liable to look to experienced international suppliers than to patronize small local companies². These smaller companies struggle to benefit from the knowledge produced by universities and research centers, as their capacity for absorption is limited by their low R&D budgets. Researchers are often more interested in perspectives of academic publication and peer recognition than in commercializing new discoveries. Moreover, in Europe, particularly during the current period of limited growth and budgetary austerity, it is difficult to persuade banks to invest in innovative projects. The venture capital market is not yet at a sufficient stage of development, due to its fragmentation across different European economies.

This all results in under-exploitation of the potential of clusters, principally due to low levels of interaction between actors³. Gaps have emerged between cluster sub-systems, resulting in insufficient knowledge production, weak network structures, differing standards, attitudes and visions, the absence of a shared language, low confidence and negative motivations. The roads to innovation are scattered with obstacles that hinder the process.

These problems affect competitiveness as well as innovation. As we have seen, the under-exploitation of cluster potentials also stems from the weakness of entrepreneurial initiatives, a significant factor in the strength of US clusters. Potential entrepreneurs often lacking technological and market knowledge need to clearly assess opportunities. This problem is magnified by the absence of efficient, structured networks, leading to a failure to organize collaborations and coordinate action.

The shortfall in cluster performance in relation to potential, in terms of innovation, may be expressed as an equation [KET 12]: knowledge shortfall + network shortfall + collaborative shortfall = innovation shortfall.

This is the reason for the dominance of cluster organizations included in “Cluster initiatives” (see Box 5.1): these initiatives enable bridges to be built between actors, foster knowledge transfer and connect clusters to global markets and international value chains. These actions are all more effective in a favorable macro-economic context.

The EC’s treatment of cluster competitiveness also needs to be considered. These remarks are equally applicable to a number of other projects carried out on clusters. Malmberg and Power [MAL 06b] indicated that this research makes use of an implicit model, which, in the European Commission text, may be written as follows:

As the authors note, we appear to be in a situation of circular reasoning. The clusters in question are presumed to lead to increased competitiveness; however, at the same time, they are defined on the basis of their competitive success, because they are productive and innovative and because localization factors produce a favorable effect once a critical mass has been reached (but how is this to be measured?) The combination of theoretical (that which is) and normative aspects (that which could be), to use Gaétan Pirou’s terminology, creates confusion. Competitiveness forms part of the definition of a cluster, and those which are not competitive are considered to be weak clusters (few assets and limited capabilities) [COM 13, p. 27], that is, as forms of organization which cannot be defined as clusters. Following this line of reasoning, it is difficult to know what to think of emerging clusters, linked only to potential markets, still at the structuring phase, and in which agglomeration and social interaction effects have yet to be observed. The same is true of technological developments, which have yet to reach a point of stability.

5.1.2. Toward a generalized ecosystem of innovation

A briefing produced by the European Parliament [REI 16] considers that “the innovation process occurs in an ecosystem in which companies, public research institutions, financial institutions and government bodies interact through the exchange of skills, knowledge and ideas”. The analysis of innovation models highlights the role of networking models, particularly in terms of extending these models toward an open innovation approach. The hypothesis according to which innovation is a distributed process, mobilizing different actors who must interact in order for the process to be sustainable, leads to the representation of a generalized ecosystem at the European level, which goes beyond that of national innovation systems. Within this ecosystem, actors are considered as sources and receivers of flows. Innovations are produced by companies (small and medium-sized companies, start-ups and big businesses) which benefit from financial and knowledge flows, notably those emitted by research and technology organizations, working under contract for big businesses.

However, this representation of Europe as a global innovation ecosystem raises certain questions. The design and implementation of policies affecting training, the quality of the economic environment and knowledge production and dissemination requires a high level of interconnection between different decision levels – European, national, regional and local – if only because innovation policies need to be adapted to national and local contexts in order to be fully effective.

Considering the case of a European research and innovation zone, there is no single solution suitable for adaptation to all of the countries involved. To attain a federal dimension, there is a need to develop research agendas in connection with problems considered to be essential and to support community research programs. This is the aim of the Flagship Initiative Innovation Union, which identified major lines for innovation support within the Europe 2020 Strategy. Given the fragmented nature of European economies, the document invited member states to reform their research and innovation systems so as to “enable interoperability at the European level”. This would be no mean feat.

We also need to address the operation of this generalized ecosystem, and its potential for articulation with a configuration of industries or activities in a cluster form. Nothing prevents us from considering that this “meta-ecosystem” might be broken down into ecosystems specific to industries, connected by complementarity flows, and to the groups of companies involved. A factor for homogenization can notably be found in the existence of strong connections between clusters, reinforcing competitiveness. As Malmberg notes [MAL 03a], empirical research indicates that “there tend to be modest commercial relations between firms within spatial clusters” and that “other types of collaboration are more common locally, but such relations extend well beyond the borders of narrowly defined regions” [MOL 03a, p. 153]. This is explained by the fact that commercial transactions (input/output) and formal collaborations tend to follow the organization of the value chain and to become increasingly global.

However, the articulation between these two levels of analysis raises two questions. First, component ecosystems must be assigned objectives, which, if not shared, need to be compatible within the context of a broader ecosystem, for example, increasing regional GDP per capita or producing goods or services for the global market (and, implicitly, increasing competitiveness), or relate to a level above and beyond GDP, including economic, social and environmental aspects⁴. The ultimate aim is to develop a positive culture of innovation, articulating the research and innovation processes with the values, cultural attitudes and expectations of European societies. This can be seen in the direction taken by the European research project, which first introduced the concept of RRI, responsible research and innovation [REI 16].

It is important to note that European societies have a somewhat mixed attitude to innovation. In the case of France (and of certain other countries), we find “a truly schizophrenic vision of innovation, which values the production of new knowledge without wishing to profit from its applications. Innovation is not seen as the transformation of ways of thinking and acting, with consequences, which inevitably lead to de-classification of products, processes and a necessary re-assignment of skills to sources of creativity. It is, above all, seen as a process of growth and preservation, taking the form of scientific progress supported by public R&D spending, and the creation of knowledge-intensive jobs, which validate the country’s areas of excellence” [GUI 13].

Second, we need to consider the factors involved in the emergence of component ecosystems. Is it better to prioritize entrepreneurial initiatives, following a smart specialization approach, or to count on the motivating power of public authorities? Once again, clear fault-lines are apparent across Europe. These questions will be considered in greater detail in the third part of this chapter.

5.2.1. Knowledge-intensive services and innovation

KIBSFs are consulting firms, brought in to solve a problem by providing (i) a knowledge-intensive service and (ii) strong interactions with the client business. However, the notion of knowledge-intensive services needs to be more precisely defined. In the case of the services mentioned above, particularly R&D services, knowledge transfer requires modes of knowledge treatment and creation, which specifically concern the form or structure of knowledge. The relationship between KIBSFs and their client companies is illustrated in Figure 5.1.

The authors break the relationship down into three stages: the acquisition of tacit or codified knowledge, knowledge recombination within KIBSFs (knowledge generated by interactions with clients is combined with existing knowledge, permitting the creation of new knowledge) and knowledge transmission to client firms. Knowledge creation is based on interactions between the supplier and the client, and this representation is thus perfectly suited to R&D services. External knowledge can only be appropriated by firms, enriching their knowledge bases, through long-term processes of interaction and information exchange, articulating the provision of complementary input with highly localized skills and knowledge.

It is important to note that the codification which underpins this schema is a complex operation, involving high fixed costs in connection with information technologies and with the processes required to create messages, build models and develop languages (concepts and vocabulary) [COW 96].

“Codification has consequences, notably the reduction of the costs involved in knowledge acquisition, reducing informational asymmetry, modifying spatial organization, and examining the division of labor. The cited authors note that knowledge appears to follow a general law of evolution, passing from a tacit form to a more systematic form which may be transmitted at lower cost. In this context, all knowledge can potentially be codified and thus transferred (producing externalities). Restrictions do not relate to the intrinsic transferability of intellectual assets (the complexity of knowledge), but rather to the fact that only economic agents in possession of the appropriate code have the ability to extract value from codified intellectual assets” [GUI 04, p. 67].

There are therefore certain hindrances to the use of codified knowledge, which must, among other things, be adequate. The innovation process is highly complex and uncertain. The results of R&D may, even after codification, be difficult to interpret and hard for a company to use. The codification of knowledge is only one of the elements involved in conditioning its transmission; the beneficiary’s capacity for absorption, assessed in terms of “necessary institutional support”, is also crucial [JOH 02].

A study carried out between 1995 and 1997 involved empirical investigations of interactions between KIBSFs and small to medium-sized companies in five regions (Alsace, Gironde, Baden, Lower Saxony and Saxony) [MUL 01]. The total sample featured 1,903 small-to-medium manufacturing businesses and 1,144 KIBSFs. Of them, 1,393 companies indicated that they had made innovations in terms of products or processes over the course of the three preceding years. Of this total, 543 small-to-medium businesses and 493 KIBSFs innovated simultaneously and underwent growth during the same period.

Client firms were active in the food and farming industry (9.1%), textiles (7.8%), wood and paper (16.3%), chemical (17.1%), machinery and equipment (17.4%) and electrical equipment sectors (13.8%). The investigation raised three significant points:

• – interactions between producers and users have a stimulating effect on the innovation capacities of small-to-medium businesses: 76.6% of the businesses working with KIBSFs introduced innovations during the period in question, compared to 60% for those working without KIBSF support;
• – firms operating in connection with KIBSFs had a higher rate of cooperation with “technological infrastructure institutions” [MUL 01, p. 1509], that is, universities and research centers. Existing interactions thus resulted in a lowering of “the cooperation barrier” between companies and institutions. KIBSFs act directly on small-to-medium companies and indirectly by promoting relationships with non-industrial organizations;
• – the final result confirms the role of the interaction process: 73.1% of KIBSFs could be seen to have contributed to innovations made by their client firms, while also innovating within their own structures.

From this perspective, KIBSFs strongly contribute to productivity in their sectors of activity. These service providers are considered to be the most innovative actors; moreover, the knowledge they produce has a wider and, to a certain extent, systemic impact [PRO 11]. This is due, first, to their operational position, at the frontline of innovative practices. Second, they assist in the “translation” of ideas and concepts produced by fundamental research into practical and marketable knowledge, which may then be used by other companies [TET 08].

Overall, the role played by KIBSFs, both in producing services and as interface agents, is significant. This provides elements of a response to Arrow’s question regarding the ways in which a firm may become more efficient in acquiring information [ARR 84, p. 145], given that the dispersion of knowledge modules may increase coordination costs for the firm using them. In essence, the grouping of specialized service providers within an organized ecosystem can lead to gains in efficiency and real savings.

Furthermore, a number of research projects have shown that KIBSFs have a high propensity to geographic concentration, notably around big cities. These companies form a network structure, with the smallest entities found at the local level. However, “high-tech KIBS show a regional commitment in that they are often created as spin-offs of enterprises already operating in the area. Moreover, the social ties of the entrepreneurs to their home district are strong in all kinds of KIBS”. [TOI 04, p. 100]. Finally, as Probert et al. indicated, the spillovers from the interactions described contribute strongly to the reinforcement of regional capacities. The co-localization of users, the availability of complementary resources, notably human capital, and pre-established collaborations are important in determining the demand for knowledge-intensive services.

5.2.2. The Cambridge cluster: structure and development⁵

The Cambridge science and technology cluster is one of the biggest in Europe. High-tech activity represented 14.5% of total jobs in Cambridgeshire in 2006, reaching 25.4% for the South Cambridgeshire area. From the outset, the companies involved adopted a business model on the basis of contractual provision of R&D services rather than standard product development.

The authors identified KIBSFs and connected companies operating in the region for more than 30 years⁶. The sample featured 10 consulting companies specializing in technological development, with a turnover depending almost exclusively on the provision of services to other firms. The four largest companies within the sample were Cambridge Consultants (CC), PA Technology Centre, TTP Group and Sagentia. Their clients were distributed across several countries, with markets in existence for more than 20 years, and more than 50 years in the case of CC. The latter, founded in the early 1960s by graduates from the University of Cambridge, “can be regarded as the origin of this type of business in the Cambridge area” [PRO 11, p. 11]. These four companies employ more than 300 people, and the other six have approximately 100 employees each.

5.3.1. Content and contribution of cluster policies

These policies focus on three main aspects: targets, the choice of tools and levels of intervention [COM 13, p. 23 ff].

The main focus is “on improving the competitiveness and economic performance of a specific cluster or group of clusters as a regional agglomeration of economic activities” [COM 13, p. 24]. The chosen criteria include vitality, assessed in terms of market performance, and importance within a regional economy from a perspective of employment and economic activity. Not all clusters necessarily satisfy these criteria, and the future potential of emerging clusters is taken into account. Possible interventions target either specific actors or cluster categories (such as R&D intensive clusters).

The tools which may be used are classified into three categories:

• – funding for platforms or CIs organizations;
• – support for collaborative actions;
• – upgrading the business environment of a cluster by funding the creation of a research institute or a workforce development program.

Actions may be carried out from a variety of levels. Local, regional, national and EU authorities have developed specific programs. There is a significant variation in levels of expertise and skills between countries, according to their degree of decentralization, the nature of political decisions and the capacity for intervention at different levels. Actions carried out in Europe highlight the main dimensions of these policies:

• – regional interventions prioritize employment levels and value creation in established clusters, without neglecting emerging clusters;
• – national programs, designed to support the efforts of regional clusters, have a more selective and better-defined character and may be oriented toward strategic activities (innovation) or actors (such as smaller businesses);
• – actions in support of national programs follow selection and funding criteria (as in the case of competitiveness poles)
• – support programs aim to create ad hoc structures with the purpose of providing tools and technical instruments for use by cluster organizations;
• – programs targeting specific sectors or networks do not have a specific geographic dimension. They are not centered on clusters, but may involve clusters as partners.

The analysis of these policies is puzzling. The existence of economic gains, in the form of higher than average salaries, employment levels or increased exports, is undeniable. These variables are generally assessed at the regional level, with the hypothesis that clusters are associated with these results. Furthermore, the overarching architecture does not appear to correspond to a well-defined guiding principle. The number and variety of targets and eligibility criteria, along with the presence of multiple levels of decision, limits the intelligibility and effectiveness of interventions.

This raises a number of questions. What level of flexibility is permissible with regard to unsatisfied criteria? How can we evaluate the emergence potential of a cluster? Do the productive asymmetries within the European space affect the possibilities for action at EU level? How can national efforts, which are fragmentary by their very nature, be harmonized in order to avoid duplication and wasting public resources? Have existing policies led to more efficient allocation of productive resources (human capital, research infrastructures, the provision of innovation-specific services) and increased regional integration in Europe?

Policies often refer to improving the cluster business environment, but the notion of a business environment itself remains fuzzy. It may be considered as a useful umbrella term, used to cover a highly varied set of factors, in which case it is difficult to see what types of actions could be taken to modify it. Furthermore, competitiveness, seen in terms of performance or as a result, is simply a symptom, not the root of the problem. “Put differently, improving an outcome such as competitiveness cannot be a sufficient justification for a policy” [DUR 11, p. 6]. We need to identify the mechanisms which lead to increased competitiveness and which may serve as anchor points for public policies, which would thus become specific to each ecosystem.

Horizontal policies, targeting sectors or sub-sectors, indistinctly target a number of clusters. The eligibility criteria used are debatable. There is a risk of creating modes of access to public resources, notably at EU level, involving a form of implicit “subscription desk”. This situation involves a “sunk cost bias” or “sunk cost fallacy”, which expresses the tendency of [decision makers]⁷ to continue with a project following every initial outlay of resources in terms of effort, funding or time [WOR 15]. These rigidification factors are strengthened by pressure from national political lobbies. However, project cancellation may entail the recognition that effort and resources have been wasted. Many examples of EU policies show an unambiguous, progressive engagement to a “failing course of action” [WOR 15, p. 185]. The European Commission itself recognizes that “if public funding is sustained even when performance benchmarks related to real market success are not met, this creates waste and distorts competition” [COM 13, p. 26].

This general view is shared by many authors, who put forward other arguments:

“Cluster policies at regional level are likely even to accentuate strongly mimetic programs of local and national industrial development – resulting in fostering knowledge base standardization, wasteful duplication and the dissipation of the potential agglomeration economies at system level – as a multiplicity of imitative local government authorities compete to attract the small finite pool of mobile capital, management and knowledge resources. The resulting duplication, unproductive uniformity and lack of imagination and vision in setting R&D and cluster priorities can be expected to produce poor results at the EU level; with most regions remaining unattractive and unable to compete with other territories to attract high value resources and to retain their best resources” [FOR 13, p. 14].

These remarks illustrate the need to look for other foundations for cluster policies.

5.3.2. A new approach based on the smart specialization strategy⁸

Our aim in this section is to provide a broad outline of smart specialization strategy (S3) and to analyze the ways in which it may be used to renew the foundations of cluster policies in Europe.