Innovation in Clusters

Innovation in Clusters
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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. <br />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.

Оглавление

Estelle Vallier. Innovation in Clusters

Table of Contents

List of Illustrations

List of Tables

Guide

Pages

Innovation in Clusters. Science–Industry Relationships in the Face of Forced Advancement

Foreword

Introduction

I.1. Innovation policies and the clustering process

I.1.1. Ensuring the legal and fiscal framework for the partnership between science and industry: governing from a distance

I.1.2. Clustering: an old idea at the heart of current innovation policies

I.1.3. Focusing on biotechnologies: catching up with the world through clustering

I.2. The cooperation mechanism in a biocluster context: from concept to reality

I.2.1. The advent of structures for science and industry intermediation

I.2.2. From the cluster concept to its realization: between adoption and resistance

I.2.3. An immersion survey: observing, interviewing and quantifying on a daily basis

I.3. Acknowledgements

1. From Industrial Districts to Knowledge Valleys: the Legacy of the Cluster

1.1. The industrial district: the oldest ancestor of the cluster

1.1.1. The economic approach of industrial atmosphere

1.1.2. The first Italian districts and their influence in France

1.1.3. The rise of districts: the end of the Fordist enterprise?

1.2. Spatial concentrations of technological activities

1.2.1. The time of technopoles: reconciling regional planning and innovation

1.2.2. A spontaneous and innovative environment conducive to a “technological atmosphere”?

1.2.3. The era of cognitive capitalism: the race for creativity of individuals and territories

1.3. The valleys of knowledge: interindividual relations as a source of innovation

1.3.1. Informal links in the heart of Silicon Valley

1.3.2. The relational logic essential to geographical proximity

1.3.3. Social capital as a driver 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

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

2.1.2. Knowledge management and its workers as a dominant paradigm

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

2.2. Global dissemination of good clustering practices

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

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

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

2.3. The French legislative framework from the 1980s to the 2010s: a favored coming together of science and industry

2.3.1. Researchers converted into entrepreneurs

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

2.3.3. A cluster for every territory

Box 2.1.A local example of a specialized life sciences cluster model: Genopole

3. The Cluster Imaginary: Tools, Local Narrative and Promise

3.1. Performative instruments: benchmarking, territorial marketing, visual instrumentation

3.1.1. Benchmarking or territorial mimicry

3.1.2. Territorial marketing: asserting the cluster’s symbolic capital

3.1.3. Visual instrumentation: the image of a dense, expanding campus

3.2. The construction of a narrative

3.2.1. Evry, the French cradle of human genomics

3.2.2. Industrial renewal: the cluster as a solution to local economic development

3.2.3. The rhetoric of technological backwardness: overcoming French scientific slowness

3.3. Promises of innovation and employment at the territorial level

3.3.1. The promise of the biocluster: a sustainable environment and the medicine of the future6

3.3.2. Becoming the capital of genobiomedicine, creating jobs for innovators

3.3.3. The naturalization of the cluster effect: an unquestionable concept?

4. Networking Systems: Repeated but Hindered Initiatives

4.1. Scientific and industrial administration: establishing a recurrent event

4.1.1. The emergence of an intermediary figure: the cluster administrator

4.1.2. Networking and renewing acquaintances among cluster members through regular events

4.1.3. Fostering communities of practice: the creation of thematic bodies

4.2. Sharing a technology platform: mutualization or collaboration?

4.2.1. Resources as an intermediary: a policy of sharing expensive equipment

4.2.2. Platform usage: service provision before collaboration

4.2.3. Equipment demonstrations: connecting or making visible?

4.3. The institutionalization of conviviality: “la vie de site”

4.3.1. Bringing together and involving employees from different backgrounds

4.3.2. Building emotional and community connections through volunteering and sport

4.3.3. L’Escale, a space of sociability revealing professional hierarchies

5. Scientific Competition and Economic Competition: Social Fields Spanned by Internal Struggles

5.1. Asynchronous organizations and work rhythms

5.1.1. Dissonant work schedules between companies and laboratories

5.1.2. Belonging to the large biotechnology family or disciplinary demarcation logics?

5.2. A scientific field built from struggle and precarity

5.2.1. A workforce that is becoming precarious

5.2.2. Scientific work destabilized and concealed by competition

5.2.3. Researchers and industrial collaboration: an unequal commitment

5.3. An unstable relationship between economic development and industrial secrets for companies

5.3.1. Individuals are increasingly encouraged to start a business to escape unemployment

5.3.2. The fragility of the male 30-something entrepreneur

5.3.3. Intense activity marked by the search for financing and competition

6. The Avoided Cooperation

6.1. A patchy local network

6.1.1. What type of organizations for what type of interactions?

6.1.2. Scientific and market relations behind informal interindividual exchanges

6.1.3. More outwardly looking organizations

6.2. Cooperation prevented by paradoxical demands

6.2.1. Additional time pressure

6.2.2. A disembodied objective between prescribed program and real work

6.2.3. Loyalty and performance objectives towards the employer

6.3. Avoidance strategies

6.3.1. Avoiding scientific and technological issues

6.3.2. Cluster administrators: between belief and lucidity

Conclusion

References

Index

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Smart Innovation Set

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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.

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