2.1.1. Porter’s cluster: the rapid spread of success stories

The cluster concept became popular in 1990 with Harvard University (Boston) Business Strategy professor Michael Porter. In The Competitive Advantage of Nations, he defines a cluster as “a geographical concentration of companies and associated institutions, interconnected within a particular field and linked by common elements and complementarities” (Porter 1990). At first glance, this definition suggests that the cluster is a rediscovery of established evidence of the district, technopole or innovative environment concepts (Torre 2006, p. 17), but differs in that it is organized and instituted. Strengthening the links between cluster actors is a policy objective of Porter’s work, which advocates public intervention in the constitution of these local clusters (Renaud 2015, p. 67).

Moreover, unlike the work on districts, clusters do not refer only to groups of enterprises in the same sector but also to research structures, as was also the case in the work on innovation environments and technopoles. Nevertheless, in his initial approach, Porter describes intense internal competition as well as essentially vertical coordination processes (with subcontractors, suppliers, etc.) along a value chain. Productivity, through cost reduction, appears to be the priority issue, ahead of innovation, which assumes a more horizontal coordination, enabling the transfer of knowledge, primarily between laboratories and enterprises. In 1998 and 2000, Porter clarified his definition by emphasizing the combination of cooperative and competitive relationships between firms, underpinned by formal or less formal social networks, all of which lead to better learning and diffusion of innovation (Leducq and Lusso 2011, p. 7).

Porter’s approach has been widely accepted, and two dimensions have been particularly examined: territorial anchoring and market-oriented relationships. Indeed, there are works that raise the question of the relevant geographical scale of the cluster (Cooke 2001) and focus on the relationships maintained within the cluster and not outside it. At the same time, following on from Porter, clusters are analyzed from the perspective of transactional and contractual relationships (Depret and Hamdouch 2009, p. 23). Based on the work of Nalebuf and Brandenburger, management professors from Yale School of Management and Harvard Business School (Nalebuf and Brandenburger 1996), Gordon and McCann apply the principle of coopetition to cluster relationships. By contracting the terms “cooperation” and “competition”, this oxymoron, which is no longer an oxymoron in the authors’ view, refers to the long-recognized phenomena of alternation between states of competition and cooperation (Blanchot and Fort 2007, p. 166)¹. Gordon and McCann therefore consider the relations between actors in a cluster from the perspective of coopetition (Gordon and McCann 2000). In management and economic literature, there are also a series of authors who clarify Porter’s concept and emphasize the innovation (Leducq and Lusso 2011) or high-tech cluster (Depret and Hamdouch 2009). In particular, Achermann sees “a triple helix that turns at full speed to accelerate the emergence of innovations” (Achermann 2014, p. 12). These works identify the regional scale as relevant, as it would allow for alternation between global and local resources (Veltz 1996; Cooke 2001), and assess that innovations require a sufficient “critical mass” of skills and therefore a greater need for coordination than in less R&D-intensive sectors.

Based on a statistical study of the evolution of the number of scientific publications on the subject of clusters, Suire and Vicente demonstrate the rapid diffusion of the concept. By researching contributions in economics and innovation policy analysis, they observe a clear upward trend between 1998 and 2008, followed by a period of stability up to the present day. Beyond the metric dimension, the authors inform us that, in terms of content, the concept of networks as “market or non-market relationships of economic actors who participate in a collective process of innovation and knowledge exchange” (Suire and Vicente 2015, p. 95) runs through this literature. The main common thread in this work is the idea that, without the networking of actors on a local scale, the potential for innovation remains limited. Progressively, clusters are perceived according to an approach that is no longer exclusively competitive, but more reticular (Leducq and Lusso 2011, p. 22).

The cluster concept is all the more popular as it appears at the heart of so-called “success stories”. Indeed, some studies consider that the idyllic vision of the cluster, which is so popular with many academics and policymakers, needs to be qualified, since it is analyzed from the perspective of successful examples²: “Silicon Valley, Route 128, San Francisco Bay Area, Research Triangle Park, Italian districts, Baden-Württemberg, etc.” (Depret and Hamdouch 2009, pp. 8–9). This focus on success stories and the strong tendency of literature on links between universities and industry to focus only on success stories (Renaud 2015) are accentuated by the emphasis on the cases of Stanford University and MIT (Owen-Smith 2003). These two examples hold a significant place in the literature describing the economic and territorial development of Silicon Valley and Route 128 in the United States (Saxenian 1994) and contribute to a positive and pacified image of cooperative relations between universities and businesses:

While there are many works on these two successes, there is little or no trace of the failures, i.e., the more numerous cases where the relationships between universities and business, on which regions or municipalities had gambled, did not produce such fruitful results (Trépanier and Ippersiel 2003, p. 77).

Indeed, based on a corpus of works on the relations between research institutions and business between 1995 and 1999 in scientific journals and those published by governmental or international organizations (OECD), Trépanier and Ippersiel show that the objective is often the same. They are almost exclusively success stories, more than half of which are located in the United States (37 out of 67), followed by Europe (but only 22 mentions, including 8 in England) and the remaining 9 in the rest of the world. The sample is over-represented by English-speaking countries, which implies industrial and university systems with state-of-the-art equipment and scientific and financial resources; indeed, large companies and high-tech SMEs occupy a predominant place. On the academic side, these are institutions with national and even international reputations, with significant scientific output, rather than regional universities focused on training and with less celebrated research teams (ibid., p. 78). The corpus highlights a series of success stories whose initial ingredients remain exceptional: prestigious research and scientific production, large industrial groups, state-of-the-art equipment and a market facilitated by an economic liberalism specific to English-speaking countries. In this literature, the model most often put forward is that of Silicon Valley. Its success is said to have been made possible by a relational density built up over the years (Suire and Vicente 2015):

From the 1960s to the present day, California, more than any other place on the planet, has brought together the scientific and cultural factors favorable to the networking of individuals and to the proliferation of techno-social innovations that have resulted (Dagnaud 2016, p. 15).

This narrative is also based on the idea that Silicon Valley and the advent of the internet are the result of a counterculture comprising hackers and hippie communities whose project was, above all, utopian rather than economic (Flichy 2001). The enthusiasm for these “cultural creatives” (Ray and Anderson 2001) of Silicon Valley should be qualified by the historical weight of public investment. The Internet, or even GPS, on which GAFAMs³ proliferate today, relies not only on these “garage innovators” but also on large U.S. federal state funds, from government organizations and the military (Sainsaulieu and Saint-Martin 2017, p. 13). Moreover, the literature places little emphasis on the economic failures experienced by the cluster of excellence, particularly during the dot-com bust, which wiped out about 127,000 jobs (9% of regional employment) in the space of 18 months and half of the gains accumulated over the period between 1998 and 2000 (Leducq and Lusso 2011, p. 18). Silicon Valley thus found itself in a situation of over-specialization or mono-activity (Torre 2006, p. 30). By focusing on the nascent Web industry, it was at the mercy of either a change in the competitive universe or a financial collapse. Furthermore, it is interesting to mention that one of the founding fathers of Silicon Valley, former Stanford University vice president Frederik Terman, was asked by Bell Labs to replicate the California Valley experience in the supportive environment of New Jersey, but never succeeded (Duranton et al. 2008: 19). Although the process of imitation and replication seems risky, it is clear that much of the literature on clusters has rarely been analyzed beyond the success stories (Torre 2006, p. 18).

2.1.2. Knowledge management and its workers as a dominant paradigm

Porter’s cluster is also part of a series of models designed for public action. The most famous are the triple helix (Leydesdorff and Etzkowitz 2000) and the New Production of Knowledge (Gibbons et al. 1994). The triple helix refers to an imaginary figure of dynamic interdependence between the three spheres of industry, the state and science, and conveys the idea of a social order increasingly based on knowledge (Shinn 2002b, p. 25). Knowledge is thus generated through fluid relationships between the state, markets and science. The model does not differentiate between the academic, industrial and governmental spheres, but this configuration of three interdependent and equivalent helices is the product of a slow and progressive historical process. For the authors of the New Production of Knowledge, it is more of a rupture. They show how we have moved from Mode 1, characterized by a closed academic world with watertight disciplinary fields, to Mode 2, structured by interdisciplinarity and interactions with industry. The thesis of Gibbons, Novotny, Limoges, Schwartzman and Trow has been successful in the English-speaking and Nordic countries by proposing the simple opposition of the old university, where researchers asked and answered questions, to the new, where society poses problems and researchers, as experts, provide solutions (Schultheis et al. 2008). In his conclusion on the collective work Le cauchemar de Humboldt. Les réformes de l’enseignement supérieur européen (The Humboldt nightmare. European higher education reforms), Yves Winkin emphasizes the prescriptive character of Mode 2, compared with the more descriptive Mode 1: “Today, there is no longer a colloquium of the OECD, the World Bank or the European Union on ‘knowledge management’ that does not use the contrast between Mode 1 and Mode 2” (Winkin 2008, p. 200).

At the dawn of the 2000s, the drive towards the neoliberal “knowledge-based economy” was thus at the heart of international bodies such as the OECD and the European Union (Bouchez 2016, p. 33). One of the first references to this now-used term is an OECD report, Employment and Growth in the Knowledge-based Economy, coordinated by economists Foray and Lundvall (Foray and Lundvall 1996). They highlight the correlation between the so-called knowledge-based sectors of activity and economic growth. Their thesis is the result of a series of seminars organized by the OECD. They developed the idea of a knowledge economy as a set of activities oriented towards the production, distribution, exchange and consumption of goods and services linked to intangible goods and/or cognitive capacities linked to knowledge. The authors have subsequently supplemented the OECD report with several publications (Foray and Lundvall 1997; Foray and Cowan 1998; Foray 2000; Lundvall and Archibuji 2001; David and Foray 2002). In these works, it is mentioned that current economies are directed towards knowledge production and no longer towards industrial production, which has been left to the economies of emerging countries. This postulate therefore implies the emergence of a new economic discipline. With reference to the development of the industrial economy during the 1820s, at the same time as the advent of large-scale industry, so the knowledge economy is emerging at a time when economies are progressively based on knowledge production:

The knowledge-based economy corresponds essentially, in each country, to the production and service sectors based on knowledge-intensive activities. These are usually identified by combining indicators of knowledge production and management, such as research and development (R&D) expenditure, the employment rate of graduate workers and the intensity of use of new information technologies (Foray 2009, p. 3).

In order to define these knowledge-intensive activities, the authors rely on the earlier work of the economist Fritz Machlup, who includes education, communication activities, data processing equipment, information services and other information-related activities in this sector. In 1962, Machlup showed that this industry accounted for 29% of GDP in the United States in 1958 (Machlup 1962). Foray takes up this typology and completes it by affirming that knowledge-intensive activities proliferate in other production and service sectors.

The paradigm of the knowledge economy is more a particularly operative discursive category than an analytical framework. Indeed, it is becoming a watchword that makes innovation a requirement, “a major factor of economic competitiveness that tends to become the almost unique way to survive and prosper in highly competitive and globalized economies” (David and Foray 2002, p. 14). Additionally, within the framework of the knowledge economy, much of the economic and management literature at the end of the 20th century considers innovation to be the result of information and knowledge sharing within networks (Badillo 2013, p. 25). Indeed, although the sectoral basis of the knowledge economy is indeed based on science and technology, it also develops thanks to territories that have been able to set in motion a dynamic of agglomeration of the resources characteristic of this economy, namely highly qualified personnel, research and development laboratories and innovation services (Foray 2009, p. 5).

The knowledge economy is thus based on another concept derived from management sciences: the category of “knowledge workers”. Works on the post-industrial era (Touraine 1969; Cohen 2006; Rifkin 2012) show the tertiarization of the economy and the increase of service workers in finance, health, administration, etc., and refer to these types of workers. However, it was one of the fathers of modern management, Peter Drucker, who first used the expression knowledge workers in 1957. He defines this category as individuals who apply what they have learned in the development of concepts, ideas and theories, rather than through manual skills. Also, according to Drucker, unlike manual workers, organizations should not tell these knowledge workers what to do, but provide them with the right conditions to emulate, apply their knowledge and ultimately increase their productivity (Drucker 2010). Robert Reich defines the knowledge worker as a symbol analyst, in the sense that these workers create or manipulate symbolic representations, particularly with computers (Reich 1997). In a similar vein, Jeremy Rifkin, a specialist in economic forecasting, speaks of “abstraction handlers” capable of using computers and networks to identify and solve problems (Rifkin 1997). In this management literature, the emphasis is on the economic role of these workers: they are the ones who create value based on their knowledge and creative work. Knowledge workers’ main activity is to sell their intellectual output to customers (Bouchez 2004). However, this definition excludes a great many individuals who also produce knowledge, such as teachers or librarians, for instance.

Whether we adopt a narrow definition (limited to the commercial or technological spheres) or a broad one (from doctors to artists via financiers), these knowledge workers are often characterized by a particular way of working and their lifestyles. These dimensions can be found in the rather vague concept of the creative class developed by Richard Florida. Alongside the working class (which is said to be shrinking and which only accounts for 20% of employees) and the service class (in which he groups fast-food workers, cleaning and security companies, office workers, etc.), which is growing fast (45% of the working population), there is also a growing group of individuals who use creativity in their work, which he calls the creative class (one-third of the working population) (Florida 2002, p. 235). According to the author, the challenge for public decision-makers is to attract this class by investing in cultural facilities of interest to them (theaters, museums, trendy bars, cultural events, sports facilities).

In France, the work of Pierre-Michel Menger takes up the concepts developed by Florida. He too considers engineers, artists and scientists as “the hard core of a creative class or an advanced social group”, whose creator appears as “the exemplary symbol of the new worker” (Menger 2003, pp. 7–8). Menger particularly dwells on the artist who, through the precariousness and instability of his employment, represents the symbol of the professional of the future, that is, inventive, mobile, unresponsive to hierarchies, motivated, caught up in an economy of the uncertain and more exposed to the risks of interindividual competition and the insecurities of professional trajectories. A number of criticisms have been leveled at this and particularly Florida’s work: the correlation between cultural life and economic dynamism, and in particular the example of Montreal, which is very dynamic culturally, but whose economic growth remains weak, is often put forward in order to testify to the weakness of the argument (Vivant 2006); the simplistic notion of social class, which in reality is only an amalgam of intellectual occupations (Durand 2016); or the statistical construction and indicators used to evaluate the presence or absence of the creative class (Levine 2004). The most recurrent criticism remains the broad nature of the general theory. This criticism is not unique to Florida, who, in spite of this, has and continues to be invited to lecture to a number of local public policy makers. Overall, much of this literature, especially management, seems to use theoretical nesting that remains vague.

2.1.3. A theoretical and practical toolkit provided by researcher-experts in clustering

The literature on clusters has, and continues to enjoy, some success in the management and economic academic community, as well as among public policy makers, as Torre describes very well in the following excerpt:

The centrality of geographic clusters has often remained far removed from the understanding of mainstream economists […]. We had to wait for the work […] on economic development processes, which focused on the phenomena of spatial concentration of innovation or research activities. This is Porter’s work (see, for example, Porter 2000) on clusters, which has had an even greater impact than Krugman’s, since it is not limited to the economic discipline alone and directly influences the principles adopted by policy makers at both local and national levels. Clusters are now considered as the basis for local and even national policies in many countries (UK, Germany, Netherlands, etc.). For example, they have been used as a basis for the recent reflection on ‘Local Production Systems’ in France and can be compared to the new ‘Competitiveness Clusters’. More surprisingly, they are often considered as major development tools by the major operators of the globalized economy – see OECD 2001 and 2005, or World Bank 2002 (Torre 2006, p. 16).

Indeed, whether referring to local and national policies or international organizations, the cluster seems to give “turnkey” solutions to political leaders. This is the observation made by Shinn in his analysis of the work on the triple helix, in which he shows that public decision-makers must work on finding small variations in the concept (Shinn 2002, p. 28). Business leaders, politicians and administrators are invited to constantly rethink their directions, through, as Shinn tells us, symposia (including three international symposia on the triple helix) (Amsterdam 1996; New York 1998; Rio 2000) and publications related to these issues, in order to help decision-makers in the conduct and application of the concepts. Regarding the cluster, it would seem that the recipe is as simple as relying “on the resources of its regional territory, located at the intersection of the local environment and global forces” (Leducq and Lusso 2011, p. 23). Indeed, the authors add that “all territories, including the least developed, have their chances, whether they are old industrial regions or emerging countries” (ibid.). This capacity for declination relies on the deliberately vague character of the cluster, which enables it to easily adapt to different local situations. Martin and Sunley rightly show that the vague and incomplete definition of the Porterian cluster has greatly contributed to its popularity, as it can be used in a wide range of cases and interpretations. Nevertheless, they consider that it is precisely the elasticity of the notion that makes it a “chaotic concept”, since its universalist character can be a potential source of ambiguity and amalgam:

However, although the definitional and conceptual elasticity of the cluster concept can be seen as a positive strength, in that it enables a wide range of cases and interpretations to be included, we consider it to be problematic. The concept has acquired such a variety of uses, connotations and meanings that it has, in many respects, become a ‘chaotic concept’, in the sense of conflating and equating quite different types, processes and spatial scales of economic localisation under a single, all-embracing universalistic notion (Martin and Sunley 2003, p. 71).

As a result, we can see that, worldwide, political institutions, the European Union and national and local governments advised by their experts (OECD, World Bank, regional development agencies, etc.) are implementing clustering policies. As part of this process, Porter, Gibbons, Florida, Novotny, etc. are consulted, even hired, by these different political bodies in order to apply this global trend: “Clusters, it seems, have become a worldwide fad, a sort of academic and policy fashion item” (Martin and Sunley 2003, p. 4). These decision support experts are frequently referred to as “management gurus”, who come to the rescue of private managers and public institutions (Bruno and Didier 2013, p. 72). They are located in, or on the frontier of, the academic world and enjoy a significant audience within policy arenas, offering a “scholarly package with a practical purpose” (Sainsaulieu and Saint-Martin 2017, p. 14), for which innovation is the raw material. This metaphor leads Saint-Martin and Sainsaulieu to identify them as “packaging manufacturers”⁴. This figure of the researcher–expert is similar to that of the secant marginal, in that it is:

A stakeholder in several interrelated systems of action who can, as a result, play an indispensable role as an intermediary and interpreter between different, even contradictory, logics of action (Crozier and Friedberg 1977, p. 86).

Richard Florida is the typical example of the “celebrity consulting professor” (Sainsaulieu and Saint-Martin 2017, p. 15) who has managed to convert his research into a lucrative private speaking activity (about $50,000 per conference), as reported in Le Monde on April 10, 2009, which also uses the term “guru” in its headline: “Richard Florida, controversial guru of urbanism (Richard Florida, le gourou controversé de l’urbanisme)”⁵. He has been invited to many Western cities to deploy his methods of attracting the creative class, which have enjoyed a significant rate of propagation within international, European and then national political discourse. At the same time, the work of Drucker and Porter advocated that public policymakers move away from fixing market failures to fixing network failures; the “arsenal of public incentives” thus developed a series of mechanisms in this sense, for the majority of cluster variants in most developed countries, with the aim of bringing together individuals with high innovation potential (Suire and Vicente 2015, p. 95). The vagueness of the concept makes the cluster a notion with different uses, interpretations and examples of application (Largier et al. 2008, p. 22). Its lack of robustness has lead, as we have seen, to academic criticism, “while the political world is quicker to seize on it because it ‘speaks’ better than the usual economic theories” (Lamy and Le Roux 2017, p. 94). Indeed, the concept has been widely taken up at global, European and national political levels. We will also see that the figure of the researcher–expert is at the heart of each policy level.

2.2.1. A paradigm born in the United States and forged at the heart of the OECD

French research and innovation policies are often justified by overseas examples, most often from North America (Louvel and Hubert 2016). The success stories mentioned above are indeed becoming exemplary and should be reproduced in other contexts. Louvel and Hubert have clearly shown the influence of major international paradigms on nanoscience and nanotechnology science policies. A similar influence is at work with the knowledge economy paradigm, insofar as public R&D policies in industrialized countries have reinforced the process of contextualization of science, essentially by bringing it closer to industry (Brunet 2011). The United States has been a leader in the construction and diffusion of this paradigm. As early as the 1970s, biotechnology appeared to be a source of economic development. In 1975, the Asilomar conference was held in California, bringing together researchers in molecular biology and life sciences. It aimed to anticipate, in the context of biotechnology development, the industrial, economic and social consequences of precise intervention on the genome of living organisms.

After 20 years, the accession of the Clinton and Gore Democrats to the summit of American government led to the implementation of a complex economic, financial and legal mechanism for transferring scientific resources from the public to the private sector (Uzunidis 2007, p. 54). The traditional science and technology policy (major research and development programs in the energy, defense, space sectors, etc.) was replaced by a research and innovation policy. The latter is oriented as much towards public research programs as towards business (Branscomb and Keller 1998). In this context, American economists close to the Democratic Party conceptualize the processes of transfer from one business to another, from public institutions to private entities. Avoiding mechanisms of domination and power relations, they see it as “multiform, multifunctional and multipartner cooperation” (Uzunidis 2007, p. 65).

The OECD, along with the World Bank, is one of the international institutions most involved in the dissemination of the concepts of the knowledge economy paradigm, notably by supporting the policies of the United States. In a 2006 report, the institution highlights the actions of industrialized countries that: (1) favor funding for research carried out under the supervision and control of industry, (2) reform their university systems to make them more competitive in order to improve the supply of scientific and technical services to companies and (3) promote the mobility of researchers and their involvement in business (OECD 2006). At the same time, the institution points out that certain obstacles to complete efficiency remain, such as the civil servant status that researchers often enjoy, or the evaluation of public research, which is still based on the criterion of published work and not on the contribution of researchers to industry. As early as the 1980s, the OECD, based on the US case and with the help of economists, developed the normative and neoliberal concept of national innovation systems (Lamy and Le Roux 2017, p. 70).

One of these liberal economists, John Stanley Metcalfe, defines the concept as a set of institutions that contribute to the development and diffusion of new technologies. These institutions form the framework within which government implements policies to foster the innovation process. It is a system of interconnected institutions that create, store and transfer knowledge and skills and define new technologies (Metcalfe 1995). The cultural authority of economics and the prestige of the OECD give legitimacy to all of these precepts as set down in national policies (Lamy and Le Roux 2017, p. 71). Moreover, policies to link science and industry are not only at work in the highly industrialized OECD countries. Indeed, developing countries do not hesitate to reproduce policies in vogue in more developed countries and call on experts in the field. In Asia, in particular, Michael Porter and Christian Ketels have produced a series of works on the implementation of a Cluster Mapping Project in Thailand, whose objective is to strengthen the competitiveness of the region. Using Porter’s methods, this organization is assessing the possibilities for creating and supporting cluster formation in Thailand (Leducq and Lusso 2011). This broad global dissemination is made possible because:

It comes from an organization that has managed to establish itself as an essential “interpreterˮ, but also because, as an indicative, non-binding guide to good practice used by default (there are not so many in “modelˮ markets), it leaves room for the actors who refer to it to legitimize their own political action in practice (Lamy and Le Roux 2017, p. 72).

In a context of neoliberalization of public administration, the OECD manages, through strategic documents and management tools, although not legally binding, to influence national policies.

2.2.2. To adopt OECD recommendations, or have them imposed?

In their book, Benchmarking, l’État sous pression statistique (Benchmarking, the state under statistical pressure), Isabelle Bruno and Emmanuel Didier explain that, in the context of new public management, the OECD is developing a genuine benchmarking policy (Bruno and Didier 2013). Beforehand, in their introduction, they tell us that this managerial method means “evaluating by comparison with a model, a standard, an external norm” (ibid., p. 9). In a globalized market, enterprises must no longer simply produce more, but must offer the best. In this context, benchmarking is the cornerstone of the panoply of instruments devised by management experts. This method was popularized by Robert Camp, an executive at Xerox, in 1989 to describe the identification of good practices among competitors in order to improve the company’s performance. Developed in private companies, benchmarking has been transposed to public organizations and the liberal slogan of “less government” has been replaced by the neoliberal injunction of “better government” (ibid., p. 10). Using this logic, the OECD is an organization created to produce expertise and recommendations for member states, thus aiming to standardize public policies in terms of both content and form. As early as 1994, the institution produced a document entitled “Performance Management in Government: Performance Measurement and Results-Based Management” in which government officials were urged to move “from a culture of rule enforcement to a culture of performance” (OECD Public Management Committee 1994). After two years, in 1996, in its policy paper The Knowledge-based economy, the OECD stated in its foreword:

Identifying ‘best practices’ for the knowledge-based economy is a focal point of OECD work in the field of science, technology and industry. This report discusses trends in the knowledge-based economy, the role of the science system and the development of knowledge-based indicators and statistics (OECD 1996, p. 3).

This excerpt is consistent with the idea that the OECD, lacking supranational competence and even less democratic legitimacy, would have focused on its sole source of power: the collection of “data on its richest and most industrialized member countries with a view to producing comparable statistics to support its recommendations” (Bruno and Didier 2013, p. 114). In this way, it becomes able to influence states by comparing and ranking them (knowledge-based indicators and statistics) and ultimately by forcing them to adopt the best practices it identifies and recommends. In 2014, the institution went so far as to recommend that tuition fees be increased in fields that would offer (again based on statistics) the best job opportunities (Granger 2015, p. 73). In the case of clusters, the OECD disseminates its recommendations through cluster policy guidelines (Suire and Vicente 2015, p. 97), in which we find, in addition to statistical overviews, a good proportion of the management literature mentioned above, which praises the Porterian logic of clusters and the imperative of densifying networks as a condition for their performance. In these guides, public intervention is considered to “boost the production of innovation through synergy effects that could not exist in a context of organizational isolation” (Suire and Vicente 2015, p. 97).

More generally, we observe a diffusion of these management concepts through the role of “carriers” (Winkin 2008) that certain academics have played between the scientific, economic and political worlds, either mandated or directly within global political institutions. The French academic world has paid little attention to these very Anglophone debates, and the dissemination of their concepts has mostly been done away from the academic world (Winkin 2008; Lamy and Saint-Martin 2017). In contrast, they have been at the heart of political arenas. We recall that Foray and Lundvall forged their concept of a knowledge-based economy in OECD seminars. As for the authors of Mode 2, their thesis has progressively been imposed as a flagship work, like a charter for public, scientific and private decision-makers concerned with ensuring value creation through innovation policies based on science and its high degree of dependence on other spheres, particularly industrial (Brunet 2011). This appropriation and success in Europe is all the more rapid because, at the time of the publication of Gibbons, Nowotny and Scott’s book, Re-Thinking Science: Knowledge and the Public in an Age of Uncertainty (Gibbons et al. 2003), Helga Nowotny was a member of European bodies: from 2001 to 2005, she chaired the European Research Advisory Board (EURAB), which played an advisory role to the European Commission. It is in this context, and by figures at the frontiers of science, economics and politics, that the European higher education system will be imagined through the Lisbon Strategy and the Bologna Process.

2.2.3. The European Union, sponsor of the race to the knowledge economy

Convinced by the strategies of its transatlantic neighbor, and above all in order to compete with it, the European Union launched into the race for the knowledge economy at the beginning of the 21st century, to make its territory the global nest of knowledge and ideas. The Lisbon Strategy, decided by the European Council in March 2000, aims to make the European Union “the most competitive and dynamic knowledge-based economy in the world”. From that point on, higher education appears to be the fundamental instrument at the service of the economy and leads to quantified and rational logics based on the education rate:

Estimates suggest that a one-year increase in the average educational attainment of the population translates into increased productivity growth rates of 5% in the short-term and an additional 2.5% in the long-term (Council of the EU 2004).

The Lisbon Strategy is a tool for intervention by the European Union to unify and standardize national university systems. Isabelle Bruno’s work highlights the major significance of European decisions in the higher education and research area (EHERA) (Bruno 2008). The author analyzes the Lisbon Strategy as the imposition of a neoliberal frame of reference on national systems. She shows the influence of a diffuse and non-regulatory process of Europeanization, through practices such as benchmarking (see section 2.2.2.), which invites university actors to be more competitive, in opposition with legally binding European directives. Thus, a series of major normative guidelines is being put in place and disseminated in the universities of the Member States, which Christine Musselin has grouped together under the term “scripts” (Musselin 2008).

The first of these five imperatives is “to modify the role of the state”, principally by disengaging it from university affairs through the creation of new bodies for the management and evaluation of higher education policies (AERES, ANR, etc.) leading to a policy of resource allocation and accreditation according to performance (ibid., p. 16). The second script is “to transform universities into organizations”. In France, this process of autonomy was achieved through legislation relating to university responsibilities, known as the LRU (Loi relative aux Responsabilités des Universités). The most striking example of this conversion of universities into organizations is the management of positions and people, which is no longer the responsibility of the state. Universities therefore develop rational hiring relationships and control procedures, similar to those between employers and employees. The third script is “to increase the role of stakeholders”, with the idea often taken up by public decision-makers that the university must “come out of its ivory tower”. The minutes of the European Council of March 20–21, 2003, call for “the stimulation of interactions between industry and research institutions if we are to realize our potential to create new businesses” (Granger 2015, p. 69). Here, we find the “New Knowledge Production” thesis, on the need to make research socially relevant, moving to Mode 2 of knowledge production. The fourth script is “to enter into the logic of privatization”. Universities are encouraged to “redouble their efforts to transform ideas into real added value” by adopting a funding system similar to that of the United States (European Council of March 20–21, 2003, ibid.). These funds “may derive from sponsorship, research contracts with companies or public services, or from student fees” (Musselin 2008, p. 19). On the one hand, these funds would come from:

Enterprises that could, for example, be invited to finance or co-finance equipment, schools, scholarships, curriculum renewal activities, university chairs or departments, research universities, training courses to attract students and apprentices to fields facing a shortage of skilled workers (Council of the EU 2003, p. 17).

Derived from the private sector, these techniques bring employer investment into the university, which in turn guides teaching and research and creates a competitive situation between universities to find the best investors and financial resources. On the other hand, one of the sources of funding for higher education should come from users. This second resource requires an increase in tuition fees presented as a rational “cost/benefit” calculation for the student. It is shown as vital that the latter must invest in studies in order to reap a benefit in future years on the labor market (Garcia 2009, p. 161). The Lisbon Strategy, with its dissemination of these principles of university financing, intends:

To produce economic growth through investment in human capital and technological innovation, but also to instill independent thinking into students regarding economic utility, of which the European institutions are the guardians (Garcia 2009, p. 163).

Finally, the last script is “to place oneself in a global perspective”. This quickly brings to mind the Bologna Process and its objective of harmonizing European systems in order to facilitate and intensify exchanges. In 1998, the French Minister of Education at the time, Claude Allègre, brought his German, Italian and English counterparts together to sign the Sorbonne Declaration. This declaration calls for the emergence, alongside the euro and economic treaties, of a Europe of Knowledge that includes the recognition of qualifications obtained in the European university system. On June 19, 1999, this process took shape with the famous Bologna Declaration, which was signed by a larger number of countries (29 European states, both EU and non-EU members) than a year earlier in Paris. European leaders committed to the creation of a European Higher Education Area (EHEA) based on the two fundamental principles of the Sorbonne Declaration, namely the creation of a university system organized in three cycles (bachelor’s degree, master’s degree, doctorate) and the implementation of the ECTS (European Credit Transfer System) as standards, in order to harmonize the various European degrees. This process was thus implemented during the 2000s, punctuated every two years by conferences of the signatory countries to monitor the progress made.

The Lisbon Strategy and Bologna Process thus invite universities, newly responsible for their own funding, to move closer to industrial actors and to adopt a mode of organization that originates from the private sector. Moreover, the management logic is that the regions should be “mobilized by Europe and the state in order to promote entry into the knowledge economy” (Crespy 2007, p. 27). We then observe competition between territories under the guise of territorialization of research and innovation policies, in which clusters will be mobilized (Laperche and Uzunidis 2010). Again using the logic of benchmarking, the European Union is setting up “the European Cluster Observatory, whose objective is to permanently monitor the dynamics of the most innovative clusters in order to establish a common methodology for the European Union” (Leducq and Lusso 2011, p. 16).

If we look at the cases of the OECD and the European Union, we can see that international organizations are both places where the legitimacy of ideas and their relays for dissemination are constructed (Godin 2002). As a result, science and technology policies in France are not immune to this movement of European appropriation of the Anglo-Saxon model of the relationship between science and industry. This interweaving is now considered irreversible and has become essential to change national modes of public action in order to adjust to the globalized environment (Branciard 2002, p. 34).

2.3.1. Researchers converted into entrepreneurs

Beginning in the 1980s and especially during the 1990s, France also worked to disseminate strategic science (Rip 2002), that is, “basic research conducted with the expectation that it will produce a broad base of knowledge that can be used as a basis for solving recognized practical problems, whether current or future” (Irvine and Martin 1984, cited in Peerbaye 2004). France made its first legislative breakthrough with the Act of July 15, 1982 on “the orientation and programming of research and technological development in France” (Vergès 2010, p. 17). For private law professor Étienne Vergès, by placing the discovery of new results and their dissemination on the same level, the Act introduces a paradigm shift that reduces the scope of the ethical principle for researchers. With this legislation, research organizations are authorized to set up a research commercialization service, to create development companies or even to create subsidiaries. From a statutory point of view, researchers cannot carry out public research and private commercialization activities at the same time. However, they can be seconded to a company to carry out research, development, technology transfer or knowledge transfer activities. They can also be made available for the creation of an innovation company and to then return to the public service once their task is completed (ibid., 2010).

We then observe a penetration of private legal tools within public research that shakes the scientific ethos based on the Merton model: universalism, communalism, disinterestedness and organized skepticism. The Act of December 23, 1985 on research and technological development did not create new legal tools, but extended the 1982 Act by placing the commercialization of research at the heart of the budgetary objectives of the new national research policy. Ten years later, the Interministerial Committee for Scientific and Technical Research (Comité interministériel de la recherche scientifique et technique, CIRST), meeting under the chairmanship of Prime Minister Alain Juppé on October 3, 1996, declared:

While underlining the need to maintain a sustained effort in favor of fundamental research, the CIRST considers that the research system should be more oriented toward meeting socio-economic objectives. It also emphasizes the need to commercialize the results of public research.

The text then insists on the necessary transfer of technology towards “socio-economic objectives”. The 1999 legislation on innovation and research, known as the Allègre Act, enshrined this principle. It is directly inspired by the American model, in the sense that it intends to use scientific innovations as factors for economic growth. Claude Allègre, then Minister of Research, presented the legislation as a necessity:

Let’s take a look at U.S. growth in recent years. About a third is driven by the new information and communication technologies and more than half by the high-tech industries (statement by Claude Allègre, June 3, 1999).

This speech is part of a rhetoric developed a few years earlier by Claude Allègre, notably in a book published in 1993, L’âge des savoirs. Pour une renaissance de l’Université (The knowledge age. To revive universities). In this book, Allègre delivers his vision of the French university, behind the United States, as mentioned in the above quote, and marked by its “viscosity” and inertia (Allègre 1993). He explains that the French university needs a renaissance, which must involve adapting its teaching to professional needs and opening up to the global circuit of capital (Granger 2015, p. 63). In 1999, he therefore presented the Innovation and Research Act as a way of dusting off the old university machinery and the resistance that was forming within it. This legislation sets three essential objectives: researchers with public status are encouraged to create a company or to participate in its development; the creation of innovation companies is advocated; the transfer of public research results to companies is similarly supported. Thus, thanks to measures that derogate from the principles of the civil service, which require that civil servants devote their entire professional activity to the tasks entrusted to them, research personnel can start a business as a manager or partner. It also authorizes collaboration contracts or research consortiums between the public and private sectors. It was in this context that the first public incubators were created to provide researchers and other project leaders with the necessary financial and methodological support to set up businesses and commercialize their research, using a variety of legal forms, primarily public: internal university services, private companies, associations, public interest groups, scientific interest groups, etc. In 1999, most operators had already created their own development structures, which explains this diversity of legal forms (Vergès 2010). The turn of the century saw the emergence of the first French companies created by researchers with public status and the consolidation of their support structures.

In addition to legislative and legal tools, France has set up seminars similar to those of the OECD and the European Union. Thus, in the first quarter of 2001, the General Planning Commission organized a “seminar of experts on the knowledge economy […] constituting the preliminary stage of a reflection […] on the theme of France’s integration into the knowledge economy” (Paillard 2001, p. 6). Among these experts were academics⁶, representatives of the Ministries of Economy, Finance and Industry, Employment and Solidarity, National Education, the European Commission and research institutes (in international relations, on qualifications, etc.). The seminars addressed the various themes of the knowledge economy, as conceived in supranational bodies: the spatial polarization of innovation activities, questions of cooperation and coordination in the knowledge economy, the measurement of links between private and public actors, the mobility of young doctors, to name but a few (ibid., p. 5). The experts formulated their conclusions based on a theoretical corpus similar to the management literature previously mentioned (Foray, Lundvall, Florida, Machlup, etc.), which helped to disseminate them within French institutions. In the same vein, in 2004, the Blanc Report was submitted to the Prime Minister. This document, in the wake of the Allègre Act and its public incubators, served as a guide for the implementation of competitiveness clusters policy, which grants subsidies to groupings of laboratories, training programs and businesses in a given sector, on a regional scale.

2.3.2. The university: a link in the cluster supply chain

In his report to the Prime Minister, Pour un écosystème de la croissance (For a growth ecosystem), Deputy Christian Blanc prioritized the competitiveness of territories, with the aim of remedying the social and economic handicaps that prevent France from achieving sufficient growth. He suggested developing synergies between laboratories, enterprises and training at the local level, in a competitive context. The same year, a DATAR report entitled La France, puissance industrielle. Une nouvelle politique industrielle par les territoires. Réseaux d’entreprises, vallées technologiques, pôles de compétitivité (France, an industrial power. A new industrial policy through territories. Business networks, technology valleys, competitiveness clusters) made the same observation as Christian Blanc, with both referring to the work of Michael Porter. As an example, Blanc made sure to recap on the cluster management concept:

The cluster model, which Michael Porter defines as “a group of companies and institutions that share a common field of expertise, are geographically close, connected, and complementary” is actually at the heart of economic development […]. The people who work in these institutions meet, discuss, understand and are able to engage with each other, thus enabling the development of clusters (Blanc 2004, p. 13).

The Blanc Report emphasized the regional level as relevant for the emergence of clusters and proposed several solutions for transferring a certain number of responsibilities for regional planning and the management of incentive aid to companies to the regions themselves (Jacq 2011, p. 7). This logic of regionalization of the economy also aimed to give more weight to universities by creating regional clusters, at the expense of national research organizations. Nevertheless, the state remained the main organizer of this policy, which, following a national call for projects, selected and accredited 71 competitiveness clusters. A budget of 1.5 billion euros was allocated over three years (grants for collaborative projects and exemptions for researchers’ positions in companies). Clusters were, for the most part, characterized by large, leading corporations, central to the cluster’s interaction system (e.g. Airbus for Aerospace Valley in the Midi-Pyrénées region and Sanofi for Medicen in the Paris region). While the state’s historic concern was to avoid a concentrated and unequal economic geography by supporting declining territories, it is now encouraging competition between territories in order to obtain accreditation and encouraging specialization and cooperation within the most dynamic territories in order to create interactions that are favorable to innovation (Duranton et al. 2008, p. 12). The 2009 report by senators Michel Houel and Marc Daunis gives a “unanimously positive assessment” of French clusters (Houel and Daunis 2009, p. 1) and adds that “the competitiveness cluster policy is in line with the Lisbon Strategy” (ibid., p. 65). Indeed, the reports make sure to emphasize that this is a global dynamic that should be both followed and encouraged. The Blanc and DATAR reports provide many positive examples of clusters in Europe (Catalonia, Denmark, etc.) and in the rest of the world (Australia, Brazil, etc.).

The competitiveness clusters policy was subsequently reinforced by the National Strategy for Research and Innovation (Stratégie Nationale de Recherche et d’Innovation, SNRI) from 2007 to 2012, which principally lead to the creation of thematic research alliances to coordinate research and development priorities in defined sectors. In the case of biotechnology, this includes the National Life Sciences and Health Alliance (Alliance nationale pour les sciences de la Vie et de la Santé, AVIESAN). The LRU Act has greatly increased the autonomy of universities (as well as the harmonization of courses based on the European LMD model), and their competition has been facilitated by the creation of financial (National Research Agency, Agence Nationale de la Recherche) and evaluation (Agency for the Evaluation of Research and Higher Education, Agence pour l’évaluation de la recherche et de l’enseignement supérieur) agencies. Based on the prescriptions of the Bologna Process, as is the case in other European countries (Vallier 2011), France is adopting a research system with reduced training cycles, whose content must first of all enable the employability of students and correspond to the criteria of an evaluation agency (AERES) in order to obtain, at the local level, the sufficient credits granted by a national agency (ANR). Under the guise of autonomy, universities are not, ironically, free to define their training programs, constrained as they are by the liberal logic of evaluation and calls for projects:

By assigning an imperative of competitiveness, competitiveness and performance to institutions, it [the LRU Act] has established the conditions for their managerialization based, on the one hand, on internal budgetary rationalization and steering by objectives and, on the other hand, on successive calls for projects that have enrolled them in a relentless race to obtain the means for their survival. The Eldorado promised by the transition to “expanded responsibilities and competencies” (Responsabilités et Compétences élargies, RCE), which was supposed to free universities from the shackles of the state, has, in fact, placed eight of them in a state of “supervised autonomy” under the supervision of the rectors, while the others are now experiencing the joys of seeking their own funding. Canvassing companies, begging for donations from alumni networks, increasing tuition fees, in short, “selling out”, these are the new skills that universities have gained (Bruno and Didier 2013, p. 194).

In this context, university presidents are trying to meet government requirements, while at the same time pulling their weight in an increasingly competitive world, by partnering with the local industrial fabric (Granger 2015, p. 87). The frame of reference is, in effect, that of the knowledge economy in which the university becomes a link in the “supply chain” within clusters (Lamy and Le Roux 2017, p. 103). Following the various reforms initiated by the European Union at the end of the 2000s, a policy of bringing research centers and private firms closer together was established in higher education by the EHERA (Faure 2005). The clustering policy was not limited to competitiveness clusters and took several forms. Indeed, it implied, as advocated by the Blanc Report, a multilevel dependence by the academic community on local private actors (Lamy and Le Roux 2017, p. 103). Indeed, the cluster concept was then taken up by local public authorities.

2.3.3. A cluster for every territory

Seduced by the political scope of competitiveness clusters, local authorities support, or are at the origin of, structures dedicated to innovation. This phenomenon has been reinforced since the 2010s. There are no longer any cities, regions or even departments that do not promote a local cluster. Presented as an antidote to relocation and job shortages, we can cite, in no particular order: Amiens Métropole, whose economic policy is based on three clusters (e-health, digital and energy autonomy); the city of Lyon, which is launching its “bio-district, a melting pot of biotechnological innovation”; or the Centre-Val de Loire region, which, through a call for projects, has selected a dozen clusters in several fields. The competitiveness cluster model is being used by local authorities “on the lookout for new ways to ensure growth and strengthen the competitiveness of their territories, regardless of their initial level of development” (Leducq and Lusso 2011, p. 15). Incubators that predate the Allègre Act are gradually being renamed as clusters. This is the case of Genopole, which, on the eve of the 2010s, gradually set aside its biopark name to identify itself as a biocluster. The institutional landscape of clustering attempts is therefore very varied. Groups of companies, public incubators and technology clusters are almost all listed by the Association France Clusters, whose board includes directors of several clusters (including competitiveness clusters). As a service tool for clusters, the association offers its members legal monitoring, partnership research, methodological support, strategy guides, conferences and even training. We can thus observe a form of institutionalization of territorial innovation systems.

Recently, alongside clusters, we have seen a dynamic of territorialization around spaces of a different kind: third places. It is difficult to define this fashionable concept, which can be called anything from coworking spaces, to project accelerators, to digital fabrication laboratories (the famous “fablabs”). For the most part, these are devices for coordinating and activating individuals with high technological potential (Suire and Vicente 2015, p. 111). Indeed, what distinguishes them from clusters is their ability to bring together individuals (not organizations) in an enclosed space, usually a large hub or several offices (not in the same territory) and to incite temporary proximities (Torre 2008). These places are usually frequented by mobile, highly educated, freelance or even remote workers. It is not surprising that a plethora of these places have initially sprung up in large capitals (New York, London, Berlin, Barcelona, etc.). In the space of a decade, the Paris region has seen the emergence of spaces of this type with evocative names, always recalling a relaxed and convivial spirit: the Hive, the Canteen or the Anticafé, to name but a few.

Although most of them are developed in the field of computer science and new information and communication technologies, biotechnology is also an integral part of this continuous cognitive research/technology/application of science in a logic of territorialization (Branciard 2002). Indeed, the first fablab specialized in biology was also created in the Paris region, at La Paillasse. Initially installed in a garage in the inner suburbs, it joined the majority of other third places in the Paris’ Second Arrondissement in 2014. The phenomenon is spreading to other large French cities, supported by local authorities. Symptomatic of a new way and vision of working, free from the supposedly historic shackles of enterprise (fixed working hours and places, hierarchy, procedures), these places are the factory of a new type of worker, who is free to create and undertake. Boasting a culture of “tweaking”, and a subversive spirit inherited from the hacker movement, they actually embody the heroes of unbridled capitalism (Lallement 2015, p. 400). They criticize the cluster concept and the concrete forms it can take (Martin and Sunley 2003; Torre 2006; Duranton et al. 2008; Hamdouch and Depret 2009), or the theories associated with it (Levine 2004, Vivant 2006 and Durand 2016, on the creative class). Nevertheless, despite these criticisms and proven cases of failure, the concepts of the cluster and the creative worker continue to be relayed by now-celebrated carriers from the academic and/or expert world to the political spheres.

Thus, international recommendations have direct implications on the French legislative framework and consequently on local policies. An isomorphism (DiMaggio and Powell 1983) can be observed, in which each state and/or each territory adopts clustering policies. Moreover, the logics of mimicry between territories lead most clusters to set up similar coordination and networking mechanisms (Suire and Vicente 2015, p. 114). The next chapter focuses precisely on the appropriation of the cluster concept in a particular territory.