Innovation and Production Ecosystems E-Book

Bernard Guilhon

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 223

Veröffentlichungsjahr: 2017

Beschreibung

“We do not know where Silicon Valley is really located”, Feldman writes, because these types of organization, when they are dynamic, are moving and fluid.

Innovation and production ecosystems or clusters are proliferating today because they seem to be adapted to the demands of innovation, growth and employment. The process leading to their institutionalization escapes a summary analysis of the behavior triggered by monetary incentives or, at the very least, makes it richer. The relational aspect becomes predominant, the interactions between the participants testify to the difficulty of separating the geographical and social dimensions.

In the most prominent American clusters, public/private linkages and the building of social links express the centrality of networks in the innovation process. The European vision seeks to articulate entrepreneurial discoveries with vertical public interventions. The competitiveness poles in France suffer from the fact that public choices seem to be torn between two contradictory objectives: efficiency and equity.

Leseprobe

Table of Contents

Cover

Dedication

Title

Copyright

Introduction

1 The Process of Institutionalization of Innovation and Production Ecosystems

1.1. Technologies, rules and learning dynamics

1.2. Innovation and production ecosystems and globalization

1.3. Synthesis

1.4. Conclusion

2 The Problems Raised by the Analysis of Innovation and Production Ecosystems

2.1. Justifying public intervention

2.2. Innovation and production ecosystems and open innovation

2.3. Industrial structures

2.4. Conclusion

3 American Innovation and Production Ecosystems

3.1. Characteristics of American innovation and production ecosystems

3.2. Biotechnology clusters

3.3. Conclusion

4 Competitiveness Poles

4.1. Why develop competitiveness poles?

4.2. Competitiveness poles and the legacy of

systèmes productifs locaux

(SPL)

4.3. Analyzing

4.4. Conclusion

5 European Innovation and Production Ecosystems

5.1. The cluster analysis framework

5.2. The Cambridge science and technology cluster

5.3. The foundations of cluster policy

5.4. Conclusion

Conclusion

Bibliography

Index

End User License Agreement

List of Tables

3 American Innovation and Production Ecosystems

Table 3.1. Comparison of biotechnology clusters (Great Britain, Germany and San Diego)

Table 3.2. Companies in the San Francisco (SF) and San Diego (SD) clusters in 1980, 1990, 2000 and 2005

Table 3.3. Extent and density of social networks in San Francisco (SF) and San Diego (SD) in 1980, 1990, 2000 and 2005

Table 3.4. The importance of founders in the San Diego and San Francisco clusters

4 Competitiveness Poles

Table 4.1. Public funding for the projects selected within the context of competitiveness poles: the 15 clusters receiving most funding (totals in k€)

5 European Innovation and Production Ecosystems

Table 5.1. Nature, mechanisms and expected effects of clusters (adapted from [COM 13, p. 16])

List of Illustrations

Introduction

Figure I.1. Problems tackled by this book

1 The Process of Institutionalization of Innovation and Production Ecosystems

Figure 1.1. Path followed by innovative practices

Figure 1.2. The institutionalization process

5 European Innovation and Production Ecosystems

Figure 5.1. Relationship between knowledge producers and users

Landmarks

Cover

Table of Contents

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For Elisabeth, my wife, who spent much time meticulously rereading this book, suggesting changes, and asking questions that helped me clarify my ideas.

For Alice, my daughter, who provided me with the material means that allowed me to work in good conditions at Sophia Antipolis.

For Stéphane, my son, who accepted that we often could not see each other so that I could make progress with this work.

Innovation between Risk and Reward Set

coordinated byBernard Guilhon and Sandra Montchaud

Volume 2

Innovation and Production Ecosystems

Bernard Guilhon

First published 2017 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 4EUUK

www.iste.co.uk

John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA

www.wiley.com

© ISTE Ltd 2017The rights of Bernard Guilhon to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2017947356

British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-068-3

Introduction

Economic geography is simultaneously global and local. This is what several authors have called the cluster paradox: a global economy, more complex and relying on a knowledge economy, gives a more significant role to locations. Therefore, economic geography is characterized by specialization and dispersion. A number of metropolitan areas, each of them specialized in a range of activities, seems a far more productive industrial organization than one that relies on one or two large diversified cities [POR 98]. Ecosystems are thus forms of organization that tend to multiply.

We can think about innovation and production ecosystems (to make things easier, we merge this notion with the concept of clusters) in terms of the following points. First, the literature provides several definitions. Porter’s definition is the most quoted:

“Geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (e.g. universities, standards agencies, trade associations) in a particular field that compete but also cooperate” [POR 00, p. 15].

The definition of cluster includes two aspects: on the one hand, the spatial dimension evoked by the idea of geographic concentrations and, on the other hand, the technological-economic dimension conjured by the idea of industries functionally related through companies that are involved at all stages of the value chain. In the same work, Porter redefines the idea of cluster based on the notion of geographic proximity [POR 00, p. 14], without specifying “the precise scale of this geographic concentration [which] is left to the imagination” [MAL 06, p. 55].

For the authors of this article, the gradual shift in meaning of the definition of “cluster” is confusing. The issue deserves some thought: are clusters first characterized by interconnections between companies working in associated industries or are they spatial phenomena? The economic mechanisms at work are different. In the former case, functional industrial clusters are not demarcated by well-defined geographic boundaries. ICT and the Internet in particular make it possible to establish connections between operators on a global scale. In the latter case, geographic proximity makes it easier to adopt common rules, exchange tacit knowledge and interact face to face, besides strengthening localized learning.

The success of this concept, whose definition is sufficiently vague and flexible, has allowed us to apply it to different realities and, consequently, it has made it difficult to make a precise political assessment. “The trend to oversimplify, which is linked to the popularization of the definition of cluster, allows us to find clusters everywhere” [PES 11, p. 5]. Despite these remarks, a certain number of central features are emphasized to various degrees in different works, as the vast majority of authors implicitly refer to a combination of space (geographic proximity) and system (functional relationships). These characteristics are: agglomeration economies, the relational aspect linked to proximity, the interdependence of the actors, the importance of tacit knowledge, and the dynamic externalities associated with the spillover of knowledge and leading to localized learning.

The second point concerns the nature of public intervention. If most authors agree on the need to conceive new types of interfaces between public and private actors, we still need to clarify the goals and forms of public intervention, and to analyze its requirements in terms of the decision makers’ skills and expertise.

The third point has to do with the increased effectiveness of these forms of organization simultaneously on an analytic level (the sources of these advantages) and in terms of quantification, leading to documented quantitative–qualitative case studies which, however, are occasionally limited by the information available. The field surveyed includes the United States of America, France and the European Union considered globally.

The fourth point is of a methodological kind. Each ecosystem specifically builds its assets, coherence and governance arrangements. In this field, there is no model that could be copied. In other words, the catch-up theory regarding a leading country and follower countries, based on the notion of the “advantages of backwardness”, could not be extrapolated to the context of the ecosystems. We should recall that the four advantages given by backwardness are: the substitution of obsolete technologies with modern ones, the adoption of non-technological innovations (forms of industrial organization, management practices, etc.), the capital accumulation rhythm and the growth of productivity, and the relationship between the size of the markets and technological progress.

Transposition is not feasible, as we find it hard to explain why some activities develop in some locations and not in others. As we will see, this phenomenon may even be present in a single industry. As of now, we cannot understand the forces at work in the entrepreneurial and organizational migrations between locations and to research systematically the localized characteristics that attract or drive away the investments of firms. Consequently, we simply assume that the location of the ecosystems results from the different allocation of resources, a historical event, or a political decision. Local decision makers will continue to invest and get involved in the market, but theoretical thinking is somewhat powerless in relation to “the ways of increasing effective competitiveness and influencing the best results” [FEL 06, p. 2]. In other words, the performances obtained will be ascribed to economic and/or social mechanisms, while it will be impossible to establish these relationships with confidence. The performance of the clusters will very often be measured by means of regional indicators.

The problems tackled by this book can be seen in Figure I.1.

Figure I.1.Problems tackled by this book

This diagram emphasizes that innovation constitutes at once a process and a result. The process is considered in its technological, social and organizational dimensions. These aspects include the elements such as connectedness (social capital, social networks), technological diversity (combination of complementary pieces of knowledge), shared collaboration and creation, and finally the acknowledged ability of one or more actors to act as a catalyst and direct efforts towards collective action.

The result of this process can be analyzed from a social and organizational point of view (quality of coordination, sustainability of the ecosystem, collective adaptability in the face of change) and from a technological and productivity perspective (productivity, R&D, new products, etc.).

As a process, innovation is an integral part of the conceptualization and definition of an ecosystem. The factors at work are thought to lead to performances that result from the actions taken by the actors. Let us consider an example, at first, social networks are built and structured progressively. Later on, they strengthen the coordination mechanisms (transfer of knowledge, creation of companies) and, in tandem with other factors, they are involved in the sustainability of the ecosystem.

Territoriality mechanisms are at once the result of history, the availability of specific resources, the institutional environment and the will of private and public decision makers (state, federated states, regions, local authorities). Actor-centered mechanisms have a more horizontal and transversal dimension, and they require types of coordination that allow those involved in the ecosystem, as well as external collaborations, to interact, besides making it possible to create a collectivity ability to adapt to change (technologies, products, markets). Each element is indispensable, but it only represents a necessary component of the whole and, therefore, it is not sufficient. Each element only represents a piece of the system of a localized economic development. This is in line with the remark about the emerging properties of ecosystems, based on which the resources created and accumulated, thanks to localized learning, are greater than the sum of contributions of each individual involved.

At the crossing of these two mechanisms, we can find the performances obtained: despite being hard to appreciate, they may be positive, stable and even in decline. The relational aspect that influences the quality of coordination becomes predominant to ensure a key function of governance. The interactions between those involved enable the creation of specific investments in physical and human assets, the definition of new practices and their internalization, while also avoiding opportunistic behaviors. An ecosystem organizes, in a particular way, the social distribution of risks and rewards (not only monetary) between the participants. Economic performances defined in terms of patents, new products and employment are naturally a consequence of technological development and the position of companies on the markets. They are also and most importantly the product of the quality of the relationships established within the ecosystem and, especially, of the transfer of information and knowledge – tacit and living – which are indispensable for the success of innovation. The process of institutionalization is at the center of this analysis. Thus, we can put forward a typical model, which only represents a trend expressing a general movement of consolidation and then of institutional regression. There is no single strategy that can be applied to all the clusters. Each cluster creates a distinctive approach based on its resources, specific assets and mode of governance.

The plan of this work is structured around the following five chapters.

Chapter 1 is analytic. Innovation and production ecosystems are organized forms supposed to meet the demand for innovation, growth and employment. The process that leads to the institutionalization of these forms relies on learning paths that may be hindered by the constraints that limit actors in a context of globalization, especially the consolidation of localized skills, medium- and long-term collective decisions, the gaps between the production of new knowledge and its development, and the choices made in terms of location.

Chapter 2 emphasizes the issues raised by this approach, especially the justification of public intervention, the significance of open innovation and the configuration of industrial structures. What are the goals of public action? Does it aim to fix the regional imbalances and the inequalities they generate or, rather, to favor economic competitiveness, especially by developing cooperative projects between actors and networks? Can the structure of innovation ecosystems provide a level of density high enough to meet national and global needs?

If we consider France, this structural weakness has often been noticed: “the existing interactions between certain actors of the innovation system do not allow us to draw forth enough collaboration […] what is being questioned is mainly the asymmetrical relationships between large groups and SMBs, as well as the weak links between SMBs and the world of public research (universities, grandes écoles, public research organizations) and, more generally, the relative lack of cooperation between public laboratories and companies” [FRA 16a, p. 17].

These issues have an effect on open innovation practices. Once the information produced by laboratories and universities takes on the shape of a public good, firms are encouraged to invest in spite of almost non-existent private returns for basic research developed in-house. They also affect the structure of the industrial network and the cooperative projects in R&D.

Chapter 3 takes a closer look at American clusters by highlighting their distinguishing features and studying biotechnological clusters more closely.

Chapter 4 is devoted to the analysis of competitiveness poles in France and their purpose, while also focusing on the assessment of this tool.

Chapter 5 aims to carry out this analysis on a European level, and highlight the content of the policies implemented and their recent shift towards vertical interventions.

1The Process of Institutionalization of Innovation and Production Ecosystems

Innovation and production ecosystems are emerging forms of the organization of economic activities. The abundance of research dedicated to this topic evidently shows that this is a relevant theme supposed to provide suitable answers for the issues faced by present-day societies in terms of innovation, growth and employment. The main feature of the analysis of localized innovation lies in the contributions made by several knowledge domains: geographic economics emphasizes the benefits of agglomeration economies, institutional economics outlines the path of local learning, studies focusing on governance underline the need to share responsibilities between private and public actors, and approaches based on social capital rely on proximity and the communication channels of tacit knowledge. Finally, the knowledge economy models the interactions required by the innovation process, whose intrinsic nature is prominently collective, open and built on dialog.

This chapter is situated at the interface of these different contributions. The process that leads to the institutionalization and sustainability of these types of organization relies on learning paths that may be hindered by several elements. In particular, the constraints that limit actors in the context of globalization and the impact of the choices made in terms of location immediately reveal the existence of obstacles and the flexible and adaptable characteristics of these ecosystems.

1.1. Technologies, rules and learning dynamics

These types of organization are in line with a long-term perspective. The effective development and use of technological assets requires investments that also affect other categories of assets, justifying a systematic approach that is too often ignored: human capital, transmission channels for knowledge directed at businesses of all sizes, intellectual property rights, industrial structure (well-organized product chains) and so on. By definition, diversity implies interaction between elements. We need to conceive public policies that can structure effective interactions between public and private actors in order to turn this range of assets into a system: university–industry collaborations, production of qualifications of different kinds, creation of data centers and so on. Within the context of radical innovation, sending new messages about the technological, economic and natural environment is supposed to bring about changes in the behaviors of individuals and organizations and, consequently, to modify individual and collective cognitions so as to turn them into new connected forms of organization.

Rather than relying on the preferences and predictions of economic agents, we have to admit that present-day challenges cannot be dealt with by the market forces: markets are blind and, even if they do not fail in Pareto’s sense, they are unable to provide a renewed and qualitatively different vision of economic development [MAZ 14a]. More precisely, the signals of the market are limited in terms of their ability to guide technological–economic development. Economic development does not result from natural, exogenous and existing competitive advantages, but from an endogenous creation of new opportunities that lead us to define and establish new competitive edges [ROD 11]. Nonetheless, once a direction has been identified, the signals sent by the market affect the innovation rate.

A recent research work [POW 12] analyzed the appearance and transformation of ecosystems over time by using three types of arguments. First, the diversity of organizational forms suggests the existence of different selection environments and constitutes a repository rich enough to enable the emergence of practices, standards and rules. Second, the process whereby different organizations are assembled and connected requires the presence of an Anchor Tenant[AGR 03], whose role is not to compete or dictate, according to Powell et al. This actor is situated in such a position within the system of relationships that it can gain access to other actors, and it is acknowledged as legitimate enough to act as a catalyst, direct efforts toward collective action and facilitate the growth of common resources1. Therefore, we admit that not all actors are in the same position in terms of critical resources (influence, network of relationships, reputation) and legitimacy to promote and institutionalize new practices. This may be a university, a research organization, a private company and so on. Finally, taking part in multiple activities facilitates the transposition of ideas and models from one domain to another and creates new possibilities that lead the system toward recombination or a changeover.

This means that, leaving the creation of complementarities aside, we should attach the greatest importance to the mechanisms through which public and private actors interact. The diffusion of new practices belongs to learning dynamics structured in three phases:

– framing. This phase involves elaborating new concepts (cognitive mechanism) and new representations of an activity, creating legitimacy and promoting agreement. In this context, laboratories (companies, universities) tend to direct their R&D efforts toward the formulation and hierarchization of problems rather than their solution. Complex problems require a theorization that needs an organizational environment favoring the exchange and recombination of knowledge [FEL 14b];

– the resources and complementary actors involved in a process are combined by establishing new norms and professionalizing the actors in relation to the new dynamics;

– the progressive coordination of the activities based on rules facilitates the creation of a network, organization of skills and adoption of good practices. This last aspect raises the issue of governance and, in particular, the question of sharing and using aggregate information.

The fact that innovative practices may be regarded as public goods within an ecosystem or, in other words, that the innovation made by an actor does not decrease the possibilities offered to the other actors implies that the collective performance is improved when information about these practices is shared. Even if we assume that this information is shared, nothing allows us to claim that there will be a convergence toward optimal practices [LAZ 11]. The type of learning needed in a changing environment is based on the idea that the actors of an ecosystem have multiple connections and a “limited attention span”. If innovative practices can be easily observed, the individual ability to process information will be limited in relation to the quantity of information available. As the authors of this article point out, everything depends on visibility (“A can emulate B if and only if A observes what B is doing”) and, consequently, on the nature of innovation [LAZ 11, p. 315]. Innovative practices can be more or less easily observed and, even when this is the case, they tend to spread without entailing the production of firmly established information about what is actually working well. Inter-organizational relationships are therefore necessary.

The creation and diffusion of innovative practices is summed up in Figure 1.1.

We distinguish between the R&D phase, the problem solving phase and the phase involving the implementation of the new practices, as they belong to opposed approaches [NIG 14]. As for research – and, more precisely, basic research – the laws of nature allow scientists to rely on known initial conditions (the causes) to reach an unknown result (or effect). On the contrary, when we deal with technology, the desired result is known, whereas the starting conditions (the specific configurations of the components) are unknown. A wide range of notions may lead to the desired result. Technological functions are imposed upon rather than part of a unique relationship between some causes and a result. More precisely, technology is produced by making choices about operational principles that will define the way it functions. As for radical innovations, operational principles are chosen at the top of the hierarchy. This choice concerns the definition and design of the project and expresses social choices and value judgments. On the contrary, incremental changes are often reduced to their technical dimension and concern lower levels of the hierarchy. Moreover, as Nightingale aptly pointed out, innovative practices integrate tacit knowledge, which plays the role of active integrator and is not involved in inference or deduction processes. This element makes it difficult to observe innovative practices. This remark is somehow comforting in terms of innovation: the decreasing complexity of a problem is proportional to an easier dissemination of information about practices and a higher chance for the forces of conformity to prevail over creativity. On the contrary, as Lazer and Bernstein pointed out, the increasing complexity of a problem is associated with a trickier dissemination of information due to its tacit nature, while the agents will be gradually led to explore more wildly and delve deeper into the field of the problems in order to innovate. The lack of visibility about the practices increases creativity to the detriment of conformity.

Figure 1.1.Path followed by innovative practices2

This approach to the problems leads us to wonder about the boundaries of the notion of national innovation system3 [LUN 92]. The organizations and firms that adapt their organizational forms to the institutions in place can face inefficiencies when significant changes affect technologies, products, markets or the environment. Fighting against this inertia fundamentally means innovating against the logic of the national innovation system, which is what is expressed by the notion of “contra-system innovation” [HUN 11]. Bringing about differentiation in relation to the system in which the actors are involved requires, according to the authors, the creation of new organizational forms, the invention of new tools or the transformation of the existing ones. The actors are naturally limited by their institutional position, but they have certain degrees of freedom in relation to the institutions in place. It is acknowledged that they have a right to change, because the system offers them resources to take action, which can be of two kinds: (a) developing ideas, acquiring credibility and legitimacy; (b) conceiving other paths that lead to innovations, growth and employment.

We also support the acknowledgment that, unlike the other economic decisions (financial assets, expanding a company), innovation is a process that does not follow the laws of probability and whose chances of success or failure cannot be determined beforehand. Moreover, innovation does not take place at random, but it tends to create systems (an ecosystem is a relevant analytical framework in