International audienceEn 2004, le gouvernement français, s'inspirant d'expériences étrangères et des recommandations exprimées dans divers rapports, décida de rénover sa politique industrielle par la mise en place de pôles de compétitivité répartis sur le territoire national. Quatre ans après le lancement de cette politique de pôles de compétitivité, où en est-on ? Thierry Weil et Stéphanie Fen Chong, de l'Observatoire des pôles de compétitivité, rappellent ici la genèse des pôles de compétitivité en France (systèmes précurseurs, rapports fondateurs, cahier des charges retenu, jeu des acteurs et émergence des pôles). Ils présentent ensuite les premiers enseignements qui émanent du développement de ces pôles, en termes notamment de fonctionnement des projets, de pilotage et de financement, ainsi que les lacunes et incohérences observées. Ils soulignent, à cet égard, les difficultés d'une évaluation à ce stade : les pôles sont encore jeunes et des évaluations trop précoces peuvent desservir des projets pourtant essentiels. Enfin, ils s'interrogent sur la manière d'entretenir cette dynamique industrielle, qui, selon eux, passe par la stimulation de l'apprentissage des différents acteurs concernés et, à nouveau, par une vision de long terme, non focalisée sur les tout premiers résultats observés.
Title: France's Competitiveness Hubs
Abstract: In 2004, drawing its inspiration from foreign experiences and recommendations expressed in a number of reports, the French government decided on a new departure in industrial policy, setting up competitiveness hubs across the national territory. Four years after the launch of this policy, how do matters stand with it? Thierry Weil and Stéphanie Fen Chong from the Observatoire des pôles de compétitivité (Competitiveness Hubs Observatory) recall here the genesis of the competitiveness hubs (the precursor systems, founding reports, specifications adopted, interplay between the actors, and emergence of the hubs). They then present the first lessons arising out of the development of these hubs, particularly focusing on the operation of projects, steering and finance, and the failings and inconsistencies observed. In this connection, they stress the difficulty of making an assessment at this stage: the hubs are still young, and premature evaluations may do a disservice to projects that are, in fact, essential. Lastly, they ask how this industrial dynamic can be maintained. In their view, this involves stimulating learning on the part of the various actors concerned and, once again, a long-term vision not focussed solely on the initial outcomes observed
International audienceThis article explores the diversity of 66 French competitiveness clusters, which were all accredited in 2005 according to the same specifications, by characterizing the initial context in which they emerged and taking a close look at the link between this initial context and their current performance. Since French competitiveness cluster policy is based on state co-funding of R&D projects, we establish a typology based on a multiple component analysis and a hierarchical ascending classification of the R&D potential of the cluster's territory, the respective R&D efforts of companies and academic laboratories, the kinds of actors setting up the cluster and their pre-existing relationships. We then measure the differences among the five classes relating to their clusters' capacity to obtain state funding for their projects. Our results show that initial context can partially explain competitiveness clusters' performance. Competitiveness clusters in territories possessing significant R&D resources, and involving large companies capable of organizing projects, are the most efficient in obtaining state funding. In contrast, competitiveness clusters without prior cooperation experience perform poorly
French "competitiveness clusters" were set up in 2005 to strengthen cooperation between small and large enterprises, and training and research institutions working on similar topics and located in the same geographical area, with the aim of making this area more competitive and attractive through enhanced innovation. Our analysis of this set of 71 apparently similar networks has given us an opportunity to investigate the factors explaining the differences in their performance. In attempting this analysis, we encounter several difficulties, such as, how can we: (1) measure a cluster's performance? (2) characterize its context and resources? (3) characterize the governance of the network and the actions it takes? (4) deal with the fact that the network's boundaries evolve due to both the fluctuating commitment of some stakeholders and the implementation of the cluster's strategy, which changes the context and the available resources? (5) deal with actors' learning at all levels (i.e., the cluster's members, organization, rulers and fund providers), which changes the rules of the game while the game is still being played? Last but not least, the networks that we have taken to be homologous because they have been selected, labelled and regulated by the same rules, actually display significant qualitative differences. There may be different kinds of clusters following substantially different performance models. We could then define a cluster typology so that comparisons would be much more relevant between clusters of the same class. This could eventually lead us to create performance indicators adapted to the specificities of each class of clusters and improve the monitoring of individual clusters and of the national cluster policy.
No abstract
CERNA WORKING PAPER SERIES 2009-02National audienceEngineering departments often constitute the major locus for the capitalization of design knowledge. But such an accumulation may be more diffuse and spread between various actors in a regional ecosystem. In Silicon Valley, this dispersion of knowledge makes it more accessible to new actors and favors the regeneration of the region due to the emergence of new firms and technological trajectories. This analysis is drawn from the various accounts and publications of the history of the Valley which have become available.Si les bureaux d'études constituent souvent le lieu privilégié de capitalisation des savoirs de conception, cette accumulation est parfois plus diffuse, répartie entre divers acteurs d'un écosystème régional. Dans la Silicon Valley, cette dispersion des savoirs les rend plus accessibles à de nouveaux acteurs et favorise la régénération de la région grâce à l'émergence de nouvelles entreprises et de nouvelles trajectoires technologiques. Telle est l'analyse qui se dégage des différents récits et études publiés de l'histoire de la Vallée qui sont aujourd'hui disponibles.
Résumé du WP : Alors que beaucoup de pays tentent de favoriser l'émergence de clusters technologiques et d'écosystèmes de croissance s'appuyant sur les synergies entre entreprises de toutes tailles et recherche académique, la Silicon Valley est souvent considérée comme le modèle mythique à imiter. Il est donc utile de comprendre les raisons du développement exceptionnel de cette région. La littérature sur le sujet est abondante, mais suggère des explications très diverses. Nous proposons d'examiner ces récits, pour éviter de ramener un siècle de co-évolution des technologies, des institutions, des communautés professionnelles et des marchés à quelques recettes simplistes conduisant à des prescriptions inefficaces pour les politiques publiques. Cet examen critique de l'histoire de la Silicon Valley permet aussi d'en souligner quelques aspects moins connus comme la capacité de Stanford à apprendre de son environnement industriel ou la manière dont certaines compétences acquises ont facilité de nouvelles trajectoires technologiques
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