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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Forged at the heart of international political bodies by expert researchers, the innovation cluster concept has been incorporated into most public policies in industrialized countries. Based largely on the ideas behind the success of Silicon Valley, several imitative attempts have been made to geographically group laboratories, companies and training in particular fields in order to generate "synergies" between science and industry. In its first part, Innovation in Clusters analyzes the infatuation with the system of clusters that is integral to innovative policies by analyzing its socio historical context, its revival in management and its worldwide expansion, looking at a French example at a local level. In its second part, the book explores a specialized biotechnology cluster dating back to the end of the 1990s. The sociological survey conducted twenty years later sheds a different light on the dynamics and relationships between laboratories and companies, contradicting the commonly held belief that innovation is made possible by geographical proximity.
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Table of Contents
Cover
Title Page
Copyright
Foreword
Introduction
I.1. Innovation policies and the clustering process
I.2. The cooperation mechanism in a biocluster context: from concept to reality
I.3. Acknowledgements
PART 1 Persistence and Renewal of the Cluster Concept in Contemporary Innovation Policies
1 From Industrial Districts to Knowledge Valleys: the Legacy of the Cluster
1.1. The industrial district: the oldest ancestor of the cluster
1.2. Spatial concentrations of technological activities
1.3. The valleys of knowledge: interindividual relations as a source of innovation
2 The Management Roots of the Cluster and Its Worldwide Dissemination
2.1. An economic and management concept destined to become a public action mechanism
2.2. Global dissemination of good clustering practices
2.3. The French legislative framework from the 1980s to the 2010s: a favored coming together of science and industry
3 The Cluster Imaginary: Tools, Local Narrative and Promise
3.1. Performative instruments: benchmarking, territorial marketing, visual instrumentation
3.2. The construction of a narrative
3.3. Promises of innovation and employment at the territorial level
PART 2 Prevented Synergies: the Case of a Biotechnology Cluster
4 Networking Systems: Repeated but Hindered Initiatives
4.1. Scientific and industrial administration: establishing a recurrent event
4.2. Sharing a technology platform: mutualization or collaboration?
4.3. The institutionalization of conviviality: “
la vie de site
”
5 Scientific Competition and Economic Competition: Social Fields Spanned by Internal Struggles
5.1. Asynchronous organizations and work rhythms
5.2. A scientific field built from struggle and precarity
5.3. An unstable relationship between economic development and industrial secrets for companies
6 The Avoided Cooperation
6.1. A patchy local network
6.2. Cooperation prevented by paradoxical demands
6.3. Avoidance strategies
Conclusion
References
Index
End User License Agreement
List of Illustrations
Chapter 1
Figure 3.1. Changes in square footage dedicated to research structures in the Un...
Chapter 4
Figure 4.1. Cluster workers’ participation in professional events (source: graph...
Figure 4.2. Type of platform usage by users (source: graph constructed from ques...
Figure 4.3. Institutional affiliation of the 2016 Platform Day participants (sou...
Chapter 5
Figure 5.1. Age of company directors (source: graph constructed from the Genopol...
Chapter 6
Figure 6.1. Network between 42 biocluster structures
5
. For a color version of th...
Figure 6.2. Intra- and extra-cluster organizational relationships (in France and...
List of Tables
Introduction
Table I.1. Summary of data collected
Chapter 6
Table 6.1. Typology of network structures
Table 6.2. Typology of cluster interactions
Guide
Cover
Table of Contents
Title Page
Copyright
Foreword
Introduction
Begin Reading
Conclusion
References
Index
End User License Agreement
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Smart Innovation Set
coordinated byDimitri Uzunidis
Volume 36
Innovation in Clusters
Science–Industry Relationships in the Face of Forced Advancement
Estelle Vallier
First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUKwww.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USAwww.wiley.com
© ISTE Ltd 2021
The rights of Estelle Vallier to be identified as the author of this work have been asserted by her in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2021943916
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-625-8
Foreword
It is a peculiarity of these pandemic times, but the word “cluster” has certainly never been in such widespread use as it is today, from epidemiological science to the most common conversations, describing the development of a virus called Covid-19. However, in other times and places, and with regard to other fields of human activity, this fetish word has been used to saturation point, even if today, in certain situations, it gives way to a more trendy word: ecosystem. We may as well admit that the more than extensive meaning of this term is quite appropriate, given the various projects commissioned by any number of organizations or specific groups. This is particularly the case in those localized instances where science, industry and public authorities are, in certain fields of technosciences, attempting to work together, as much by smart invocation as by physical methods of communication. Their ambition, resolutely symbolized and displayed on their letterheads or websites, is to attract, fertilize and develop innovation aimed at certain fields of science, which, ultimately in the form of goods, will be commercialized with the help of devices, procedures, advice and financial resources.
This is indeed the purpose of this rich and fascinating book by Estelle Vallier, structured in two parts and six chapters. Its title clearly invites us to explore the lineaments of this type of three-way collaboration dedicated to the production of innovations, to study its various mechanisms and movements, to understand its concrete pathways, to consider the institutional and economic goals it demands and, finally, to identify the obstacles that arise in practice for the parties involved in order to better understand them. In short, to shed new light on the processes at work which, to begin with, we would summarize under the title Technoscience Clusterization.
Certainly, some may argue that a vast literature already exists on this subject and that there is no need to attempt to further understand what has already been understood and, to a certain extent, the first two chapters of this book could bear witness to this. However, far from the sometimes condescending repetitions and unstimulating writing conventions, on the contrary, an astute state of the art is presented to the reader. Articulated through both historical and international perspectives, it offers from the outset a point of view and, consequently, an intelligibility so that this global phenomenon of technoscience clusterization can be understood, which, nevertheless, because it cannot be grasped on an international scale, is rooted in national and local institutional and organizational logic. It therefore enables soothing discourse to be avoided and, in contrast, an understanding, on the one hand, of how this accumulated knowledge, particularly in management sciences, has been able to accompany and justify its general design, from which some miracle had been expected, and, on the other hand, its concrete and localized implementation by ad hoc public policies. It is clear that the approach chosen by Estelle Vallier is congruent with the deployment of her research; for, rather than aiming to celebrate once again the success of this three-way marriage, her ambition was to renew questioning by seeking to interpret the difficulties that the actors in this complex relationship face in practice in order to collaborate. This is indeed the central issue that is anticipated from institutionalized science–industry relationships looking to produce any desired innovation.
This was the starting point of this research, described in the Introduction, for it was necessary for a cluster pilot study to question its own role, to succeed by depending somewhat on an ambient imagination (an ideology?), made to measure like any readymade garment, before being able to call upon the critical and interested gaze of social science. This book is specifically the end result of such sociological research, certainly commissioned, as well as negotiated. This research is rooted in France’s best-known biocluster, which possesses the legitimacy that comes from its 20 years of existence: Genopole, based in Evry, in the south of Paris. In practice, the research posture of active participation in cluster coordination, while at the same time succeeding in maintaining a distance from it, could be more than embarrassing, given the risk of not being able to keep the two together over the long-term. Methodologically, this dual position has, in the end, enabled this collective role, the initial aim of which was the steering of the cluster, but entirely diverted towards the success of the relationship between science and industry, to be explored in context.
Therefore, we can say without further ado that the challenge has succeeded. This result, through the detailed analysis developed throughout the chapters of the second part of this book, leads the reader to explore the different levels of intermediation through which any institutionalized cluster pilot operates. As a result, the different problems and obstacles that resolutely stand in the way of the imagined and imaginary road of the uninterrupted flow of innovations from science to industry, in which it continues to believe, are revealed. Throughout the chapters, the reader will be aware of this need for creativeness on the part of the cluster’s operational teams, in order to keep the flame of successive attempts to establish links alive. The picture that emerges is similar to the reproduction of the legend of Sisyphus. This is evidenced by the many meeting mechanisms, tools and instruments of all kinds, all of which are aimed equally and simultaneously at academic laboratories and enterprises (mainly start-ups) that have been labeled, rather than those that are in the process of being labeled. Moreover, but no less important, lies a paradox specific to this instituted and organized process; for if the stated aim, the “cluster effect”, is to generate innovations from this networking, without this aim being able to be truly achieved to its desired extent, what hinders or even prevents it, without knowing how to precisely identify its contours, obliges the intermediary to demonstrate innovation himself in order to succeed in finding the right passage. However, it is one thing to note a certain impasse in this path and quite another to explain it. Perhaps at this nodal point, armed with the analysis, we see the specific and advantageous contribution of social science, singularly of sociology.
The final two chapters propose unravelling what is constantly being tied, rewoven and replayed by the practices of the actors themselves: scientists, startup entrepreneurs and cluster administrators. It is clear that what appears to them as an enigma pushes them to redo what has already been done, through the use of different devices. The path taken by Estelle Vallier to provide reason to this process, which is to an extent blocked in on itself, is to question cooperation, as a generic relational mode in a world given over to competition.
However, there is no better plot, including in social science, than to leave it to the reader to discover the mechanisms and the solutions that result from them. I will therefore refrain from going any further, since the reader of this book could be a sociologist, of course. Let us hope, however, that beyond the legitimate concern of satisfying the interest of subject matter specialists, practitioners of technoscience clustering will themselves be able to discover, from another perspective than their own, this intertwining that they contribute to produce, together, in their day-to-day relationships. For, if some consider that sociology is a possible weapon for transforming social practices, there are indeed some reasons. The simplest to state, but certainly, it seems to me, the most difficult to implement, is this: above all else, the role of sociology is to contribute to enriching the reflexive process of the action of any individual and/or collective. In this, it is fully involved in the social future and must claim it. There is no doubt that this book bears the mark of this!
Philippe BRUNETLaboratoire interdisciplinaireSciences Innovations SociétésGustave Eiffel UniversityAugust 2021
Introduction
Innovation is as much an overused term as it is an economic watchword: “We must innovate, be innovative… or disappear!”1 It is a sign of economic recovery, dynamism and technological potential for a better world. One of the main levers of innovation policies in industrialized countries is to organize meetings between researchers and industrialists, particularly within innovation clusters that bring together companies, laboratories and training within a particular sector. Thus, the validity and legitimacy of these mechanisms, which take shape in dedicated locations, formerly called technopoles, now known as clusters, no longer need to be proven. Tomorrow’s economy, like a promise that is always being renewed, is germinating in these places through the combined effects of innovation, its territorialization and the power of the intricate and informal relationships that are generated there. This book is particularly concerned with the articulation between the promise of innovation and the interactions observed within a particular cluster.
I.1. Innovation policies and the clustering process
In France, innovation clusters are the result of a legislative and fiscal framework that has encouraged closer ties between science and industry for more than 30 years. In this context, the state, as well as international and European authorities, is particularly encouraging territories to develop clustering policies. However, this logic is not a new idea and has been the subject of work since the 19th century, under other names. The term cluster, on the other hand, was conceptualized in North American management literature in the 1990s and has found favor in most public policies in industrialized countries.
I.1.1. Ensuring the legal and fiscal framework for the partnership between science and industry: governing from a distance
In recent decades, most industrialized countries have opted for strategies that boost the potential outputs from innovators or creators (goods and services resulting from innovation) by legislating and investing in them. The main public investment consists of aligning the creation, or scientific discovery phase as closely as possible to its technological and industrial application. Efforts are essentially expected from researchers, who are encouraged to become entrepreneurs, or at least to make their research immediately available for industrial use (Stehr 2000). Thus, the expression “contextualization of science” (Gibbons et al. 1994) has been coined, within European institutions, to describe the process of heteronomization of science by other social fields, principally economic; it must be accountable to society and, above all, be involved in the commercialization of its results (Lamy and Shinn 2006, p. 47). In France, the 1999 Innovation and Research Act, also known as the “Allègre Act”2, is presented as the significant legislative tool that guarantees a better contextualization of science. Indeed, it enables researchers, based on their scientific results, to create a company and/or to register patents. In order to support these new kinds of entrepreneurs in the creation of their start-ups, the Act provides for the reinforcement of the creation of public incubators. These structures are intended to provide, not direct financial, but indirect resources to assist in the setting up of a company: legal advice, construction of a business plan, filing of patents, search for public and private funding, etc. Incubators also provide privileged access to material resources, such as premises or equipment. This legislative framework complements the advantageous tax structure provided to research and development (R&D) since 1983 with research tax credits (crédit d’impôt recherche, CIR). Tax reduction, which is granted to companies based on their R&D expenditure, is the largest financial investment by the government in this area, especially since the 2008 reform under President Sarkozy3. In a 2013 report4, the Court of Auditors highlighted the significant tax advantage represented by CIR, which primarily favors large companies5, without any real impact on the recruitment of scientific personnel in the private sector, particularly young doctors. Despite numerous warnings and limitations, demonstrated on several occasions by research actors, including a report by the CNRS and a survey report by the researchers’ association “Science en marche (Science on the move)” (Métivier 2015), and by institutional actors6, it was renewed by the Valls government in 2015. This renewal demonstrates the political vision of innovation and its public governance, which, despite criticism from higher education circles, favors the commercialization of research within private companies.
Indeed, French innovation policies are characteristic of governmental adherence to neoliberal rationale. Often represented as a doctrine of state withdrawal, it does not, however, manifest itself in a paralysis of public authorities. On the contrary, their involvement is redeployed and takes a different form: paradoxically, “while it advocates the withdrawal of politics and the state, neoliberalism frequently results in their reaffirmation” (Epstein 2006, p. 96). Rather than guaranteeing the distribution of wealth among its territories and population, the state is actively providing a legal, social and fiscal framework to foster attractiveness, competitiveness, innovation, entrepreneurship, etc. (Bruno and Didier 2013, pp. 38–39). To do this, the state governs remotely (Epstein 2006) via high-tech Colbertism (Cohen 1992) in sectors deemed promising for the economy of the future: digital, biotechnology, nanotechnology, Big Data, etc. It is becoming an actor in global competition, through a selective location policy, which aims to attract the most innovative companies and the most affluent social classes to these territories, in order to stimulate a new development model (Brenner 2004). This is by no means specific to France. The scope of success stories such as Silicon Valley, and the legend around the innovators who have emerged from it, generally ignores the massive and historic public investment in the United States (Mazzucato 2015). Thus, the European Union, through two major projects, the Bologna Process and the Lisbon Strategy, intends to provide the framework for the development of a knowledge-based economy. Public policies in favor of the concentration of innovative activities on technical–scientific–industrial sites (Sainsaulieu and Saint-Martin 2017, p. 11), or, in a word, clusterization, represent one of its tools. France has not escaped this and is renewing its policies, as it did in the past with the nuclear and aeronautical industries, by relying on the concept of clustering in order to develop and position innovation (Lamy and Le Roux 2017, p. 89).
I.1.2. Clustering: an old idea at the heart of current innovation policies
High-tech clusters have been the subject of a wealth of research, principally in economics and management sciences, as well as in geography and sociology. Much of the literature can be traced back to the neoclassical economist Alfred Marshall and his concept of the industrial district, considered to be the ancestor of the cluster. As early as 1890, he defined it as coordination by the market and the reciprocity of a social division of labor between small specialized firms within a large productive process (Benko et al. 1996, p. 120). His approach was revisited in the 1970s by Italian7 sociologists and economists as a framework for analyzing their research on the Third Italy8, which specialized in traditional (clothing, footwear, leather, furniture, etc.) or more modern (small-scale mechanics, electrical engineering, etc.) activities (Daumas 2007). Based on Marshall’s work and his analysis of the province of Prato in Tuscany, Beccatini defines the Italian district form as follows:
A socio-territorial entity characterized by the active association, in a circumscribed and historically determined territorial area, of a community of people and a population of industrial enterprises. In the district, unlike what happens in other environments, such as the manufacturing city, community and business tend to, as it were, interpenetrate (Becattini 1989, p. 15).
This idea of interpenetration between economic structures and individuals is taken up again in policies of technopolization and then clusterization. While the work on districts was aimed at analyzing existing environments, technopoles are the first political attempts to develop niches of specialization, in order to foster social and economic environments conducive to exchanges. Moreover, it is no longer just a matter of prioritizing inter-firm relationships in the Italian districts, but of reproducing the American science–industry relationship models. The most analyzed, of these, during the 1990s, were Silicon Valley in the San Francisco Bay Area (Saxenian 1996; Ferrary and Pesqueux 2004) and Route 128 in Boston. In these studies, the emphasis is on the concentration of companies in the same sector, as well as on the geographical proximity of research laboratories and educational institutions, so that informal relationships emerge that promote innovation, collaboration and the creation of businesses. In 1990, in his book Competitive Advantage of Nations, Michael Porter placed the local concept of the cluster at the center of his theory of competitiveness within the context of a globalized economy (Porter 1990). His definition is internationally understood by public decision-makers in industrialized countries:
Clusters are geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (for example, universities, standards agencies, and trade associations) in particular fields that compete, but also cooperate (Porter 1998b, p. 4).
Porter’s conceptualization is seen in some geographic literature as a belated rediscovery of well-established evidence (Torre 2006, p. 17), including analyses of districts or technopoles. Nevertheless, this updated definition has been taken into account by public policy to the point of becoming a normative concept. It satisfies the ambition of reindustrialization, based on scientific innovation and a flexible network of small and medium-sized enterprises (SMEs) adapted to the global economic system (Benko and Lipietz 1992), notably in Germany (skills clusters), Japan (knowledge clusters) (Forest and Hamdouch 2009) or even in the United Kingdom, where it inspires New Labour in its objective of renewing industrial policies (Zepf 2011, p. 187). In France, the cluster concept also emerged at the end of the 1990s under a socialist government9. Some aspects of the concept are alluded to through, as seen above, the creation of public incubators in the Allègre Act of 1999. In 2004, the concept was firmly established at the heart of the competitiveness cluster system. A characteristic of remote government, the state intervenes by calling for projects, designed to grant subsidies to selected candidates representing consortiums of companies, laboratories, organizations, associations, etc., from the same sector of activity. The call for projects is both a way to empower local actors, while also reducing their autonomy, insofar as the competing territories must rigorously respect the specifications established by the state if they wish to be selected (Epstein 2006, p. 108). In 2004, of the 105 applications submitted, the state selected 66 (there were 67 in March 2018)10. Applicants are well aware of the U.S. references linked to the competitiveness cluster system, and this is how the Valley projects appear: Aerospace Valley, Alsace Biovalley and even Cosmetic Valley.
Once they have been put in competition with each other in a general call for projects, the territories develop strategies to find the “right ingredients”11 that promote innovation at the cluster level. The three essential dimensions of the typical ideal cluster are industry, science and education. They are embodied by companies, usually a network of SMEs and very small enterprises (VSEs), but the presence of large groups is often desirable for the cluster’s reputation and attractiveness, by both private and public research laboratories, and by renowned higher education institutions12. Among the definitions of clusters found in the literature, and without claiming to be exhaustive, the definition given by Lamy and Le Roux in 2017 appears to be one of the most complete:
It is therefore a question of a favorable environment that is not limited to elementary communication infrastructures but which benefits from the immediate proximity of specialized actors and expertise. These benefits are expected in the form of a ripple effect (called a “networkˮ effect by economists) by mutual reinforcement of several factors: productivity gains (through proximity, mutualization and/or reduction of intermediaries); the pooling of skills (under the auspices of the availability of human resources, but also of the maintenance of a permanent motivation based on challenge and elitism); an intensification of the circulation of information (which is important for prospecting and coordinating markets, but also for sharing tacit knowledge), etc. The aim is to obtain chain reactions of innovations, each one reverberating in this environment and acting as a sounding board to encourage others. The attractiveness of the cluster and its self-amplifying character must reach a critical mass providing a sustainable global competitive advantage (increasing visibility and therefore attractiveness, etc.) (Lamy and Le Roux 2017, p. 91).
Here, we see the concept of an environment that is “favorable” to innovation, where geographical proximity is a key factor in the interpenetration of the various neighboring structures. This intricacy enables the sharing of equipment and the circulation of individuals between structures. Underpinned by implicit knowledge sharing, it leads to innovations, which are formal and wealth-producing and which reinforce the attractiveness of the cluster.
I.1.3. Focusing on biotechnologies: catching up with the world through clustering
This book is particularly concerned with the contextualization and clustering of the life sciences. A major turning point for the life sciences occurred in the 1980s in the United States with the passage of the Bayh-Dole Act, which allowed universities to patent publicly funded research (Gaudilliere and Joly 2006). The patenting of life forms has thus played an important role in bringing together the life sciences and their technological application, commonly known as biotechnology. This is understood as “a set of procedures implementing biological knowledge for a transformation of the living body: gene therapy, cloning, genetic modification of organisms, etc.” (Keck 2003, p. 182). It is identified as a field with a high technological potential, capable of “irrigating large areas of the economy” (Branciard 2004, p. 10), its aim being to make living organisms a factor of production like any other. Since 1983, the Ministry of Research has notably been one of the institutions involved in promoting biotechnology through organizations such as the National Scientific Research Centre (Centre national de la recherche scientifique, CNRS), the Atomic Energy Commission (Commissariat à l’énergie atomique, CEA), the National Agronomic Research Institute (Institut national de la recherche agronomique, INRA), the National Health and Medical Research Institute (Institut national de la santé et de la recherche médicale, INSERM), universities, foundations, etc. In addition to the creation of a national committee on these issues (the “biotechnology boom” program)13, the state committed itself to promoting biotechnology on October 17, 1990. Hubert Curien, Minister of Research at the time, launched the National Human Genome Program in the Council of Ministers (Guthleben and Faou 2011). The aim was to set up a coordinated strategy with all the laboratories and institutes already involved in biological research on the genome. The program also aims to coordinate cooperation with the United States around the Human Genome Project, which aims to establish complete DNA sequencing.
Biotechnology generally refers to health-related production processes, but in reality, it covers other industrial sectors, primarily biopharmaceuticals (therapeutic products, diagnostics, medical devices, etc.) and the biofood sector (Heil 2010, p. 240). The former comprises two types of actors: small innovative firms often originating from academia (spin-offs) which, in most cases, once the fundamental research has been completed, are bought out by “big pharma”, which takes over the costly processes of the clinical phase, production and marketing. The latter addresses human nutrition, agriculture and the environment (production of food based on microalgae, detection of plant pathogens, biorefining, detection of pollutants from modified living organisms, etc.).
Biotechnology assumes both a strong academic base and interaction with the medical and industrial worlds (pharmaceuticals, therapies, agrifood, agrochemicals, environment, bioenergy). It is therefore positioned between a world that guarantees diversity (academic research) and another whose challenges, conversely, relate to standardization (medical and/or industrial application) (Branciard 1999, p. 3).
In order to resolve this conflict between standardization and the maintenance of diversification, public action mechanisms create conditions for the coming together of laboratories, universities, companies and equipment in localized spaces (biotechnology clusters or bioclusters). The cost of equipment, in particular, would explain the need for agglomerated networks in the case of biotechnology, in order to pool vital, cutting-edge instruments (Aggeri et al. 2007b, p. 202). Finally, the dominant argument in the 1990s was that there was too great a gap between scientific production in the life sciences and its commercialization, which was reflected in an insufficient number of start-ups and patent applications. Particularly in the case of biotechnology, the rhetoric is as follows: “The articulation of science with industry is not self-evident: a forcing seems necessary” (Brunet 2011, p. 2). From then on, bringing together two worlds that ignore each other requires incentives and, in particular, the creation of an intermediation structure between science and industry.
I.2. The cooperation mechanism in a biocluster context: from concept to reality
Originating in the United States, the cluster concept has become a worldwide phenomenon. In France, it has been supported by public authorities through the creation of intermediation structures in charge of strengthening the relationship between science and industry.
I.2.1. The advent of structures for science and industry intermediation
The literature uses the term “hybrid organization” (Branciard 2009) or “hybrid organism” (Leydesdorff and Etzkowitz 2000)14. Very quickly, within institutional vocabulary, the term “incubator” has become a cornerstone of science–industry rapprochement policies (Shinn 2002, p. 28). Nevertheless, among these different terms and, in a sociological approach, that of “public intermediation structure”, Brunet pertinently evokes the intervention of these structures between (“inter”) two quite distinct worlds (Brunet 2011), in order to facilitate arbitration intended to reconcile them (“mediation”)15. Moreover, this name highlights the public character of these institutions.
These structures, which are generally financed by local and national governments, can be found in various forms (associations, mixed syndicates, mixed economy companies, etc.). Most were created in response to calls for projects from competitiveness clusters. However, some were created before, and in parallel with, these clusters. The latter are anchored in a regional framework, but there are other structures for which territoriality is defined on a conurbation or a department scale. Most operate with an accreditation policy to attract laboratories and companies. Once accreditation is obtained, the new member organization benefits from services offered by the cluster (preferential rents, access to specialized and shared equipment, a computer network, etc.). When local geographic clustering is carried out, most structures rely on the “total network” (Suire and Vicente 2014). The relational density between the three actors of science, industry and training, enabled by geographical proximity, is touted as a major condition for innovation. Nevertheless, many intermediation structures struggle to meet this objective and find themselves confronted with the following organizational difficulty: how to create cooperation when it does not occur spontaneously through geographical proximity. This question echoes sociological literature, which highlights the absence of spontaneous links between spatial proximity and social interaction, notably in the study of the politics of large urban areas (Chamboredon and Lemaire 1970). Work on clusters also emphasizes the relationship or otherwise between, for instance, geographic, organized and cognitive proximity (Talbot and Kirat 2005; Bouba-Olga and Grossetti 2010; Torre and Zuindeau 2012).
This was the problem encountered by Genopole, a life sciences biocluster located in Evry, in the Essonne region of France, the main field of investigation for this book, which is the result of a thesis in sociology funded by Industrial Agreements for Training through Research (Convention industrielle de formation par la recherche, CIFRE) and which reports on the results of an immersion survey conducted over three and a half years, between November 2013 and April 2017. Genopole is a cluster that has already been studied in the literature, particularly on the political, economic and social conditions of its creation and institutionalization, mainly in the work of Anne Branciard (1999a, 1999b, 2002, 2004, 2009), as well as of Ashveen Peerbaye, which sheds light on the instrumental arrangements put in place by the cluster (Peerbaye 2004). Genopole has also been the subject of a comparative analysis of the transformation of science into technoscience, based on the cases of Evry, Laval (Quebec) and San Diego (California) (Heil 2010). There is only one other study that deals with its interactional dimension in a comparative approach. The article in question focuses on the “social capital of entrepreneurs as an index of cluster emergence” in a “comparative analysis of the transformation of two bioparks into bioclusters: Kobe (Kansai, Japan) and Evry (Paris region, France)” (Lanciano-Morandat et al. 2009). In their conclusion, the authors state that, although they have their own national characteristics, neither the Kobe nor the Evry bioparks can be categorized as innovation clusters as defined in literature insofar as:
They are still only aggregates of diverse entities with episodic relationships between them; in short, partial clusters […]. Both parks are struggling to integrate the entire innovation process, which, as Porter suggests, is a condition for their constitution as clusters. In addition, both parks have the weight of the state in their constitutions in common, the role of certain institutions in their creation, and their location on sites that have neither a tradition nor particular resources in terms of innovation (Lanciano-Morandat et al. 2009, p. 200).
In addition to the fact that the authors rely on the definition proposed by Porter to identify, or not, the clustering process, the idea of transformation from biopark (geographical grouping) to biocluster (interactions) is also strongly present in management literature (Hamdouch 2007). These concerns are also at the heart of current biocluster issues. Indeed, considerable space is given to this subject in formal and informal discussions within the cluster, warning about the difficulties of mobilizing companies and laboratories for joint meetings.
I.2.2. From the cluster concept to its realization: between adoption and resistance
This book therefore rightly proposes to observe and report on the application of the cluster concept within a specific field. The aim is to revisit the construction of this public action mechanism, which seems to be unanimously accepted, at least in discourse on innovation policies, and to confront it with the dynamics of cooperation in one of these clusters. The purpose of the book is thus reminiscent of the study of the “editing work” of foreign examples (Sahlin and Wedlin 2008). This work consists, particularly in the field of science policy, of adapting a public action mechanism already implemented abroad to the context of another country, region, city, etc. The authors speak of operations of decontextualization and recontextualization of international policies in order to inscribe them into another national or local framework. The work of editing allows us to focus on the actors who construct and “edit” these foreign examples, and on the importance of the latter in the adoption of funding policies, in particular. Séverine Louvel and Mathieu Hubert have particularly shown the role of foreign examples in the implementation in France of nanoscience and nanotechnology steering (Louvel and Hubert 2016). In the same way, this book proposes to revisit the construction of the cluster concept on an international scale and to find out who the main authors and disseminators are. However, it also proposes to report on the reality experienced by those who work within these geographical clusters.
To do this, the second part of the book draws more on the literature on the sociology of work than the first part, which is mainly based on the sociology of science; effectively, how to impose a cooperation system on individuals who work for different employers (public laboratories, start-ups, SMEs, private laboratories, associations, etc.) and who, what is more, belong to two different social fields: scientific and economic. The field survey shows that these individual affiliations are often in contradiction with the cluster’s objective of promoting synergies. Recourse to the work of sociologists who have already highlighted this type of paradoxical injunction specific to modern management (Linhart 2010, 2015; de Gaulejac and Hanique 2015) or disembodied management (Dujarier 2015) has made it possible to study the effects of the networking system on the experience and practices of individuals in the context of the cluster.
I.2.3. An immersion survey: observing, interviewing and quantifying on a daily basis
This survey, which was carried out as part of a CIFRE sociology thesis, is based on a variety of empirical material and combines qualitative (semi-directive interviews, participant observation, documentary and archival database of the biocluster) and quantitative (questionnaires and network analysis) methods.
On the qualitative side, the survey is based on 45 semi-structured interviews with a large proportion of laboratory directors and company managers, as well as with scientific directors, operations managers, chief financial officer (CFOs), communication and/or marketing managers, platform managers, research engineers, doctoral students, management assistants, physicians, etc.
These interviews were supplemented by a long phase of participant observation as a doctoral student at Genopole. This immersion was both conducive to the observation of official speeches (during colloquia organized by Genopolemeetings, visits by elected officials, etc.) and to participation in informal discussions. The confrontation of these two types of material gave rhythm to the research as a dialectic between political discourse and the reality of practice. In addition to observation, being on site provided access to certain documents archived by Genopole (activity reports since 1998, meeting minutes, press releases, digitized official documents, etc.) and to its internal database (management charts, files of accredited members, working documents, etc.).
In parallel, more quantitative material was collected over various stages of the survey. The first was a questionnaire, completed by the Genopole teams, on the expectations of employees in terms of coordination between the site’s structures, completed by 534 people. Although the sociological scope of the survey has its limits (due to its operational purpose), the number of respondents allows for a significant increase in generality.
Nevertheless, the quantitative material relies primarily on the network analysis method, which enables social interactions to be formalized using nodes and links on a graph. Nodes typically represent individuals or institutions, with links representing a particular type of interaction between two nodes. This method was adopted following the completion of a significant amount of qualitative work. As additional information became less and less significant with each new interview (saturation effect), it became essential, not to find missing data, but to observe the state of interactions at the cluster level. A relational database was therefore created on the basis of the ethnographic work carried out beforehand, and completed using the results of two questionnaires. The first, intended to observe the network at the inter-organizational level, was sent to all Genopole company and laboratory directors, the second to almost all the cluster’s employees via the Genopole intranet site. The first survey provided relational data for 42 of the site’s organizations16. The second survey collected responses from 102 people. However, the anonymity of the questionnaire did not enable the network to be mapped, but we were able to construct additional statistics based on the characteristics of the individuals interviewed and their relationship with the cluster17.
Table I.1. Summary of data collected
Method
Material
Period
Observations
Research Officer attached to the General Management of Genopole
November 2013 to April 2017
Consultation on expectations in terms of coordination
534 responses from cluster workers
February to May 2014
Documentary and archival data
Genopole archived documents and internal database
May 2014 to April 2017
Semi-structured interviews
45 interviews
May 2014 to April 2017
Individual questionnaire for network analysis
102 responses from cluster workers
February to April 2016
Organizational Questionnaire for network analysis
32 responses from laboratory and company directors in the cluster
February to April 2016
Since clusters cover multi-scale (organizational and individual), as well as historical and institutional dimensions, methodological diversity appears to be essential in order to understand a plurality of sociological mechanisms. Taking only a single approach does not make it possible to determine a set of dynamics, as the authors of the article focusing on the social capital of entrepreneurs (Lanciano-Morandat et al. 2009) point out in their conclusion. Indeed, the articulation of empirical methods (monographs, interviews, network modeling, archives, etc.) allows us to move away from current representations of clusters and to better support certain hypotheses (Forest and Hamdouch 2009, p. 17).
Within the context of this book, the methodology used allows for a better analysis of the hazards of day-to-day cooperation. Indeed, the structure of the book is based on the tension, mentioned above, between the conceptualization and concretization of the cluster. The first part reviews the socio-historical dimension of the cluster and the contemporary diffusion of its precepts on an international scale, in the French context and in a local context in Île-de-France. The second part questions the operability of the model within a particular biocluster by analyzing the resistance to cooperation in the various laboratories and companies.
I.3. Acknowledgements
I would like to thank my colleagues at the Centre Pierre Naville: Émilie Balteau, Philippe Brunet, Fabrice Colomb and Nial Tekin, for their advice and proofreading, so that the original thesis could be transformed into a book. I would also like to thank the teams at Genopole for opening their doors to me and facilitating the investigation. Finally, thanks to Blandine Laperche and Dimitri Uzunidis of the Innovation Research Network for their reconnaissance and support in the publication of this book.
1
Formula found on the back cover of Gaglio (2011).
2
The Minister of Higher Education at the time.
3
Since 2008, the CIR has been calculated as follows: it is equal to 30% of research expenditure less than or equal to 100 million euros and equal to 5% of expenditure above 100 million euros.
4
Report to the Assemblée’s nationale’s Finance Commission, “L’évolution et les conditions de maîtrise du crédit d’impôt en faveur de la recherche”, Cour des Comptes, July 2013.
5
Some companies, such as Intel and Sanofi, have been singled out in the press for laying off employees while benefiting from CIR.
6
The risks of fraud linked to the size of the system were pointed out in the report of the Court of Auditors and, in 2014, a Senate Commission of Inquiry voted not to publish a report on the reality of the deviation from CIR.
7
Arnaldo Bagnasco, Carlo Trigilia, Sebastiano Brusco and Giacommo Beccatini.
8
Expression referring to the regions of Florence, Bologna, Venice and Treviso, between the prosperous and industrial Italy of the North and the poorer Italy of the South.
9
On this point, see the article by Étienne Vergès on the links between the socialist governmental majority and research valorization policies, from the Act of July 15, 1982, to the Fioraso Act of 2013: Vergès E. (2014). Normes de la recherche scientifique,
Cahiers Droit, Sciences et Technologies
[Online]. Available at:
http://cdst.revues.org/346
[Accessed September 30, 2016].
10
The 2005 Finance Act defines competitiveness clusters as “groups of companies, higher education institutions and public or private research organizations within the same territory, committed to work in synergy in order to implement economic development projects for innovation” (Finance Act No. 2004-1484 of December 30, 2004).
11
This metaphor of a recipe to be reproduced by combining the right ingredients is present in the political discourse around clusters, as well as in that of the business leaders and laboratory directors who operate in these spaces.
12
To these three basic ingredients could be added, depending on the space, companies providing support functions (legal, IT, etc.), associations, hospitals (in the case of health clusters), administrations or public bodies, etc.
13
https://www.siv.archives-nationales.culture.gouv.fr/siv/rechercheconsultation/consultation/ir/pdfIR.action?irId=FRAN_IR_019764
.
14
In their triple helix concept (three helices representing academia, industry and government), Leydesdorff and Etzkowitz consider that the central space where the helices overlap forms hybrid organisms that are intended to make the articulation of the three spheres more effective. This concept will be further detailed in
Chapter 1
.
15
http://www.cnrtl.fr/etymologie/interm%C3%A9diaire
.
16
The sampling method and data collection are detailed in
Chapter 6
.
17
Age, gender, profession, CSP, seniority, reasons for working on the site, the different sociability places or events and the type of relationships they have within the cluster.
