Cappellin, R. (2010c), The governance of regional knowledge networks, Scienze Regionali, 9, 3, 5-42

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Scienze Regionali Vol. 9 – n. 3, 2010, pp. 5-42 Saggi e RicercheItalian Journal of Regional Science Articles

The Governance of Regional Knowledge Networks

Riccardo Cappellin*1

(Paper first received October 2008; in final form, December 2009)Abstract

This article aims to illustrate the factors determining the process of knowledge cre-ation and innovation, focusing on interactive learning, the sharing of tacit knowledge and the development of creativity. It then compares three different forms of regula-tion of economic relationships – the free market, governance, and government models – focusing on promoting a greater speed of change more than the static factors of competitiveness. Finally, it illustrates the characteristics of competence centres as a new tool of innovation policy which may be appropriate in the evolution of European industry towards the knowledge economy.

Keywords: governance, networks, innovation.

JEL Classification: R5, D8, O3.

La governance delle reti regionali di conoscenza

(Articolo ricevuto, ottobre 2008; in forma definitiva, dicembre 2009)Sommario

Questo articolo mira a illustrare i fattori che determinano il processo di creazione della conoscenza e di innovazione, focalizzandosi sui processi interattivi di apprendi-mento, la condivisione delle conoscenze tacite e lo sviluppo della creatività. Quindi con-fronta tre diverse forme della regolazione delle relazioni economiche, come i modelli del libero mercato, del governo e della governance, focalizzandosi sulla promozione di una maggiore velocità di cambiamento più che sui fattori statici di competitività. Infine, illustra le caratteristiche dei centri di competenza come nuovo strumento di politica dell’innovazione, più appropriato nella transizione dell’industria europea verso l’economia della conoscenza.

Parole chiave: governance, networks, innovazione.

Classificazione JEL: R5, D8, O3.

* Università di Roma “Tor Vergata”, Via Columbia 2, 00133 Roma, Italia, e-mail: [email protected].

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1. Introduction1

This article first illustrates an approach to innovation based on a model of knowledge creation and interactive learning, and which focuses on the concepts of the speed of change processes, tacit knowledge, and creativity. A clear distinction drawn among the phases and factors in the process of interactive learning makes it possible to highlight the territorial dimension of innovation and to identify the aims that should orient innovation policies at the regional and national level. This approach differs from the traditional one to innovation, which focuses on the inter-nal accumulation of knowledge and human capital within individual firms through the individual firm’s investment in R&D and training.

This article also seeks to illustrate that the importance of the flows and shar-ing of information and knowledge in innovation processes entails a change in the model of regulation of the market and non-market relationships among the various economic actors and in the role of the public institutions in a modern capitalist system. In particular, the article compares three different forms of regulation of economic relationships in the process of knowledge creation and innovation – the free market, government, and governance – with respect to their respective capa-bility to promote an higher speed of change and not to the static competitiveness factors such as exploitation of scale economies and price decreases.

In fact, the change in the economic scenario, the importance of innovation, and the danger of self-referential behaviours or of lock-in effects, highlight the need to reorient regional cluster policies. A shift seems to be required from the traditional approach based on strong sectoral specialization, strong territorial concentration and strong coordination of local actors (Cappellin, 1983a, 1998) to a new approach based on sectoral diversification, international openness, strong diversity of actors, and the adoption of the multi-level governance model in regulation of the relation-ships among the various regional and external actors.

Finally, the article illustrates the characteristics of competence centres as a new tool in an innovation policy aiming to promote the evolution of the European indus-trial structure towards the model of the knowledge economy.

At the empirical level, the article underlines the importance of medium-tech-nology sectors, and it is based on an empirical analysis (Cappellin, Wink, 2009) of seven European clusters in the medium-technology sectors: the aeronautic sector in Campania, Hamburg, Cardiff and Madrid regions; the optic technology sector in Paris; the auto sector in the Graz region; and the machinery for mining sector in the Silesia region. This analysis has been carried out by in-depth interviews with various regional actors, and it has allowed the identification of the character-istics of five key actors in each regional innovation system: large and small firms,

1. Acknowledgements: Support from the FP6 European project IKINET: “Interregional know-ledge and innovation networks” (http://www.ikinet.uniroma2.it/) and the FP6 European Integra-ted Project EURODITE: “Regional Trajectories to the Knowledge Economy: a Dynamic Model” is gratefully acknowledged. The author wishes to thank Rüdiger Wink and Luigi Orsenigo for their continuous, friendly and fruitful discussions.


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knowledge-intensive business services, banks, research institutions and local public institutions.

Medium technology sector include a variety of sectors according to Eurostat, such as: machinery and equipment, electrical machinery and apparatus, motor vehi-cles, chemicals and chemical products, fabricated metal products and others. Some key figures highlight the importance of these sectors for the European economy, although innovation policies mainly focus on the development of high technologies and R&D investments. Medium-technology manufacturing sectors represent the largest component (56,3%) in the trade by OECD countries, and their share in the period 2000-2005 has continuously increased from 54,7% in 2000. By contrast, the share of both low-technology and high-technology products respectively decreased from 20,1% to 19% and from 26,7% to 24,1%. High-technology sectors repre-sented only 1,08% of employment in 2006 in the EU 27 and manufacturing low technology sectors represented 7,25%. Instead, medium technology sectors had much greater importance and they represented 9,88% of EU 27 employment in 2006.

Thus, medium technology industries represent 57,9% of European manufactur-ing exports, 53,3% of manufacturing employment, and 47,.8% of manufacturing value added, while the share of high-tech industry is only 17,1% in the European manufacturing exports, 19,5% in manufacturing value added, and 5,8% in manufac-turing employment. Medium-tech sectors are characterized by numerous special-ized small firms. However, large or medium-sized firms are also important in these sectors, as for example, in the case of the automobile and machinery productions.

2. The Interactive Learning Process in Knowledge and Innovation Networks

The innovation process in SMEs and in medium technology sectors has a gradual character and it is driven by an intense interaction between the suppliers and the customers and other actors. This process of interactive learning (Lundvall, John-son, 1994; Foray, Lundvall, 1996; Lawson, Lorenz, 1999) leads to the develop-ment of “tacit” knowledge represented by a complex set of capabilities which are localized or idiosyncratic and cannot easily be transferred (Nonaka, Konno, 1998; Howells, 2002; Wink, 2003; Cappellin, 2003b, 2004, Cappellin, Wink, 2009). Because the process of knowledge creation has an interactive and a combinative character, closer geographical proximity and/or greater cognitive proximity facili-tate the interactions among various complementary actors and the combination of complementary pieces of knowledge.

Knowledge which we now have on the processes of the human mind and brain according to a scientific interdisciplinary perspective helps in explaining the relationships between economic and social actors in the processes of innovation. According the indications of the literature on cognitive economics (Loasby, 2002 and 2003; Egidi, Rizzello, 2003; Rizzello, 1999, 2003; Metcalfe, Ramlogan, 2005),

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knowledge creation is the result of a process of pattern making or of the classifica-tion and reclassification of external stimulus.

The mind is a process and not an organism. We are networks in connexion with a world of networks (Castells, 2009). The mind proceeds by networking patterns, which are stored in our brain, together with models of our sensorial experience, which we derive from the contact established with our past, present and also future experience, as indicated by our forecasts of the consequences of given signals (Damasio, 1999).

Knowledge sciences show that improvements in the human knowledge base are only possible when outside stimuli reach the individual’s cognitive system and are integrated and processed within this latter. In fact, the model of neural networks indicates that the creation of knowledge is the result of an adaptive learning or searching process which leads to new synaptic connections among various nodes. First, the joint impulses or signals coming from other firms or actors should exceed a certain threshold of intensity: a condition facilitated by the existence of common standards of communication and routines.

Any new stimulus from outside the cognitive system is then analyzed to deter-mine whether it fits into the already-existing cognitive system, categories, experi-ences, and cultural values. If it does, there begins an interactive process begins, leading to the search for consistency and compatibility. But if the stimulus is not compatible with the individual cognitive system, it is rejected. In particular, a cognitive blockade or lock-in effect may be determined by too low accessibility or too low receptivity. Accessibility is affected by the existence of infrastructures and institutions that may decrease the distance between any two nodes. On the other hand, receptivity is mainly related to the scope of the diversified knowledge avail-able to the actor or the firm considered, because such knowledge allows to identify useful forms of complementarity in relations with other actors or firms.

Hence the external stimulus should be compatible with the internal integrity or “neurognosis” (Rizzello, 2003) of the local production system and that leads to a gradual process of adaptation. In fact, the aim to preserve the personal identity in the case of an individual actor and also to ensure the survival of the organization or the local economy facing external competition may represent a powerful challenge leading to innovation.

In fact, the compatibility with other actors and the success in the adaptation leads to the creation of new connections or to the reinforcement of existing connections through the development of appropriate routines and institutions (Hayek, 1952; Nelson, Winter, 1982) which allow the saving of the limited cognitive capacity of individuals and organizations and facilitate the process of reciprocal integration (Loasby, 2003). When the same circuit is repeatedly activated, the synapses of the neurons in the circuit become stronger until the circuit becomes permanent. The consciousness of oneself– what we may call ‘personal identity, emerges from the need to integrate the largest number of mental patterns deriving from perception with the patterns stored in the memory. Newly-created knowledge must be gradu-ally consolidated into routines in order to permit further creativity (Loasby, 2007).


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On the other hand, through its mirror neurons (Rizzolatti, Craighero, 2004), our brain is able to represent the actions of other individuals when a person sees another person experiencing an emotion. This activates the processes of imitation, identification or refusal, empathy, and trust, and it is the basic mechanism leading to cooperation among humans. We may also say that the personal identity is trans-formed into a sense of common belonging or into a collective identity.

Creativity is based on on interactive learning or the integration of various abstract logical concepts and of various economic actors with different and complementary knowledge and competencies. Learning is the process whereby already existing knowledge is selected and viewed from a new perspective, and it may be recon-verted to satisfy new emerging needs.

Creativity also generates a process of differentiation among the knowledge nodes, thereby enhancing complementarity and cooperation. The differences among the various actors and firms in a knowledge economy and their interdiscipli-nary integration are part of an evolutionary process because the different technical competencies are not static but rather in continuous evolution.

This model of cognition based on interactive learning is clearly incompatible with the competitive or free market approach of standard economics. First, theories of rational choice equilibrium consider knowledge to be exogenous. They assume that cognition does not have opportunity costs, and they do not provide an economic interpretation of its origin. Second, the cognitive model is based on the human capability of pattern making and on the assumption of the fragmented nature of knowledge distribution and on selected connections among a limited number of actors. This contrasts with the perfect diffusion of information which characterizes the rational expectations model. Third, in the model of interactive learning, the relationships among the various economic actors are not based on competition and exclusion as in the market equilibrium model, but on the identification of common aims, complementarity and cooperation.

Moreover, this model of cognition is also incompatible with a hierarchical plan-ning approach based on top-down decisions, since knowledge is not a public good to be produced by public research institutions but is the result of the interaction among various private, collective and public actors. The cognitive model does not aim to indicate “what to do” or to “pick the winners”; rather, it aims to indicate “how to do” and to enhance the various factors and phases identified in the process of cognition or knowledge creation and innovation. Therefore, there is an isomor-phism between the patterns of cognition and the models for regulation of the rela-tionships among economic actors, and we may state that both the process of new knowledge creation and the relationships of cooperation, power or competition among economic agents are all based on the neural networks of the human brain.

On the basis of these principles, the model of “territorial knowledge manage-ment” (TKM) identifies a logical and temporal sequence of six phases and factors in the process of interactive learning and innovation (Cappellin 2003b and 2007; Cappellin, Wink 2009): external stimulus, accessibility, receptivity, identity, creativity and governance, as indicated in Figure 1. While these concepts have

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individually been extensively described in the economic literature, they have not previously been linked together in a coherent model based on the cognitive science literature. In fact, the external “stimulus” induced by the opportunities of demand or the pressure of competition, or change in technologies (Kline, Rosenberg, 1986; Fagerberg, 2005), determines a tension which leads to the search for a solution to the problems of the firm. This search process is facilitated by a lower geographi-cal and/or institutional distance or by a higher “accessibility” to potential comple-mentary partners (Karlsson, 1997; Howells, 2002; Boschma, 2005; Simmie, 2005; Torre, Rallet, 2005). It also requires that these latter have a low cognitive distance or an appropriate “receptivity” or absorption capacity (Cohen, Levinthal, 1990; Antonelli, 2005). Then, the creation and strengthening of a common “identity” made up of common values, a sense of common belonging, trust relatioships, and social or relational capital (Capello, 1999; Crevoisier, Camagni, 2000; Nooteboom, 2002; Capello, Faggian, 2005), is the prerequisite for cooperation among firms and their search for joint solutions. These new solutions are the result of “creativity” (Florida, 1995; Cappellin, 2003a; Wink, 2007) or of the capability of the various local actors to combine different and complementary pieces of knowledge in an original manner, and to interact within a collective learning process (Morgan, 1997; Maillat, Kebir, 1999; Geenhuizen, Nijkamp, 2006). Finally, these new ideas can be translated into economic innovations only when appropriate organizations and institutions or “governance” (Powell, 1990; Cooke, Morgan, 1998) promote the commitment of appropriate real resources and financial funds and enhance the inte-gration of the new ideas with complementary production capabilities.

For example, this model indicates, first, that the cooperation between two firms and the development of an interactive learning process between them require an external factor or problem which stimulates them to change. Second, the firms should be close each other and be able to overcome external obstacles, such as

Figure 1 - The Process of Interactive Learning and Innovation

external stimulus

receptivity

accessibility

identity

combination

interaction

creativity

governance

innovation

capabilities


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geographic distance and also differences between the languages and institutional environments of the respective regions or countries. Third, each firm should be receptive and able to understand the needs of its potential partner. Fourth, the firms should identify common medium or long-term aims and they should develop a relationship of trust and of common belonging, as in a regional community or in ad hoc groups and joint ventures. Fifth, the firms should invest and combine their respective knowledge resources and capabilities through close interaction aimed at the discovery of innovative solutions for the problems considered. Finally, the firms should negotiate and agree upon an organizational or contractual mechanism, iden-tify precise objectives, define policy instruments, and devote financial resources so that ideas can be put into practice.

Thus, the stimulus to change and innovation within firms is not only determined by the pressure of competition, the need to increase productivity and reduce costs, or the opportunity created by the supply of modern technologies and the use of modern equipment. On the contrary, especially for SMEs in medium-technology sectors, and also for SMEs in service sectors, the most important factor is the iden-tification of new markets, the aim to adapt to changes in demand and to satisfy new user needs. The desired outcome is not so much an increase in productivity indica-tors, often interpreted as a disjoint result, as a rapid and continuous innovation proc-ess where each change is the evolution of previous changes. Both entrepreneurship and governance through public/private partnerships are required to organize the joint effort of different actors and firms. The focus shifts from stimulating competi-tion among the local actors to governance or policies promoting connectivity and iterative processes of reciprocal adaptation and selection of the best productive combinations.

3. The Cumulative Character of the Knowledge Creation Process

Innovation processes have a dynamic character, as indicated by the fact that previous innovations are the basis of following ones according to a trial and error process of learning. In particular, as indicated in Figure 1, the TKM model high-lights the cumulative nature of interactive learning and the adoption of innovation as the various phases indicated above feed back on each other. In fact, innovation enhances learning. It leads to the development of the internal capabilities of the individual actors and affects the future evolution path of the innovation system considered. For example, the new knowledge created and the experience accu-mulated in the previous periods may lead to the building of interfaces facilitating accessibility to the other actors, improvement in the receptivity of the various actors to new ideas and in their capability to understand the emerging needs of potential users, a strengthening of the sense of common belonging, an improvement in the capability for joint learning and combining previous respective knowledge, and also an improvement in organizational and entrepreneurial capabilities.

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Moreover, the dynamic and cumulative nature of innovation and learning is demonstrated by the fact that the innovation of a firm changes the external selec-tion environment for other firms and, as indicated by Figure 1, it may be a stimulus for them to innovate. In fact, the last innovator may establish some new initial conditions for a new round of innovation among firms downstream or upstream in the innovation cycle (Cappellin, 2009). Each firm in its turn uses the contributions previously made by other firms, and at the same time it may lead the innovation effort, performing the role of key innovator and providing an original opportu-nity for both the other follower firms in the supply chain, which will continue the innovation effort, and the competitors, who will imitate and improve its original solutions. The almost spontaneous coordination among the firms in an innovation network allows for high flexibility and rapid change of direction by the innovation effort as it reacts to new opportunities or challenges.

In fact, just as a school of fish moves in coordinated manner and may suddenly change its direction and also its speed, so many firms and actors participate to the innovation process within a network by performing specific tasks and introducing innovation in their respective fields of activity. They procure innovative products/services from supplier firms and provide innovative products/services to client firms. Inputs sources are mutually complementary, and on the other hand clients of the products are mutually fungible. As shown in Figure 2, the selection of suppliers and of possible clients is related to their respective wait and search times, and it is affected by scanning costs and switching costs (Cappellin, 2009).

Figure 2 - The Speed of Innovation among the Firms of a Cluster in a Sequential Model and in an Interactive Model

Sequential

Interactive

The speed of the innovation process is determined by the speed at which the firm is able to orient itself and to select among possible suppliers and among possi-ble clients. This speed and the time lag between a firm’s innovation with respect to innovation in the other cooperating firms, which have previously innovated or which will use the results of that firm’s innovation, depend on the adaptive and strategic behaviours of each firm and on various types of costs and factors, such as the adjustment or switching costs (Cappellin, 1983b), in the choice of new possi-ble partners and in the change from one technological solution to a new solution within the individual firm, and the transaction costs (Williamson, 1981), which affect the coordination of one firm with the other firms. In particular, these costs


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can be related to various factors, such as: the geographical distance and the cogni-tive distance between the partners, the transaction costs involved in the negotia-tion process, the receptivity of each partner, the memory of previous experiences, reciprocal learning costs, trust relationships, the risk of opportunistic behaviour by the partners, information asymmetry, different preferences for the future and novelty, risk aversion, and also the existence of specialized services and bridging institutions.

Thus, in a dynamic environment, the creation of value and of new knowledge depends on the integration of the knowledge acquired from many firms, and the speed of innovation depends on the interaction among a plurality of actors. Inno-vation requires flexible forms of cooperation among many different private and public, regional and international actors, such as large firms, SMEs suppliers, knowledge intensive services, higher education and research institutions, financial intermediaries, public administration, and numerous other partners such as profes-sional associations and media. Innovation requires the combination of different competencies within a collective learning process, because firms must cooperate to increase and diversify their knowledge bases.

4. The Role of Institutions in Knowledge Networks

Networks are characterized by lower adjustment or switching costs (Cappellin, 1983b), and they also involve lower transaction costs (Williamson, 1981; Cappel-lin, 1988) than those of a competitive market made by isolated producers and users. While competition (i.e. free market) and monopoly (i.e. hierarchy) are static models of regulation, networks allow the regulation (i.e. governance) of the dynamic proc-esses of iterative adaptation, specialization and selection, both within individual firms and at the aggregate level among different firms.

Owing to their flexibility, networks represent the most effective form of organi-zation in promoting a fast speed of innovation. In fact, the main advantage of the network model of organization is that it ensures firms faster access to a wide range of complementary competencies existing in other firms and it removes the barriers to new products, processes and markets which could lead to a lock-in situation. Through network integration, firms are capable to decrease the resources and time for adopting an innovation, with respect to the situation in which they would have to develop these capabilities internally. Weak ties or indirect links can easily be transformed into strong ties or direct links (Granovetter, 1973) when the need to respond to external opportunities and threats make it necessary. Within networks, firms can easily change the level of cooperation with previous partners, because implicit or informal contracts are easier to adapt than explicit or formal ones. This high flexibility is a key competitive factor in a dynamic market where innovation must be adopted faster than competitors.

In this regard, institutions play a key role in the knowledge creation process. In general, rules, procedures, organizational forms, norms, and routines constitute the

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foundations of organizational behaviour. Rules and organic institutions standardize the world, and in so doing they simplify the domain in which humans use their limited cognitive capabilities. In fact, routines facilitate connections and create free time to be devoted to the explicit thinking about innovation (Hayek, 1952).

According to Loasby (2003), the maintenance of stable baselines within particu-lar domains is a prime function of formal organisations, and the appropriateness of the baseline is a major determinant of organisational success or failure. The stabil-The stabil-ity of networks is ensured by the existence of adequate hard and soft infrastructures representing public goods and which are created not only by the individual actors themselves but also by the public authorities.

Moreover cognitive processes follow an evolutionary sequence made up of vari-ety generation, selection, and the preservation of selected variants in the form of modified or novel routines and institutions (Loasby, 2003). The role of institutions is to create new routines or baselines which ensure the adaptability of the connec-tions among actors (Hayek, 1952). The existence of a well-developed institutional system consisting of various structures and infrastructures facilitates relationships and reduces transaction costs. Cognitive theories emphasise that the creation of new connections or the reinforcement of existing ones implies compatibility with other actors, success in adaptation, and the development of appropriate routines and institutions. A central concern of policy should therefore be the creation of institutions which can enhance the connectivity of knowledge.

In particular, rather diverse types of institutions play a leading role in defining a long-term strategy for the innovation of medium-technology sectors within regions (Cappellin, Wink 2009). These institutions represent the “social capital” of these regions and play the role of immaterial infrastructures which organize the knowl-edge flows among firms. Moreover, institutional solutions to overcome the lack of resources by SMEs are regionally specific and influenced by a long-term historical and cultural heritage within the region.

Regional knowledge and innovation networks induce the various actors to invest in creating or strengthening soft and hard infrastructures and routines linking them together. This makes the relationships among firms more intense or increases the speed of the flows among them. The ability of the individual firm to orient itself among the various suppliers and possible users of its products depends on the exist-ence of institutions and organizations which stimulate reciprocal trust and limit the risk of unfair behaviour, and of specialized professional services (KIBS) which perform the function of bridging institutions or immaterial infrastructures among the various firms. In fact, the speed of decision-making and coordination in a network depends to a large extent on the actor, who performs the function of leader and is able to orient the other actors.

From an institutional perspective, networks are models of the governance of relationships among various actors characterized by feedbacks in the flows of infor-mation and by incremental and cumulative processes of interactive learning and evolution. A network is a form of the learning organization which ensures greater overall dynamic efficiency.


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5. A Comparison between the Cognitive and Linear Models of Innovation

The six driving factors of the Territorial Knowledge Management are compatible but clearly different from the four factors of Porter’s diamond of competitiveness and productivity in a cluster (Porter, 1998; Martin, Sunley, 2003): firm strategy and rivalry (the nature and intensity of local competition); factor input conditions (the cost and quality of inputs); demand conditions (the sophistication of local customers); and related and supporting industries (the local extent and sophistica-tion of suppliers and related industries). In fact, these four factors are related to the concepts of identity, external stimulus, and accessibility envisaged by the territorial knowledge management model. However, they seem to indicate the effects of the local business environment on the geographical location of firms in a cluster, rather than explicitly considering the internal factors affecting the behaviours of firms and other regional and external actors in the processes of knowledge creation and innovation, such as the concepts of receptivity, creativity and governance.

From a methodological perspective, the cognitive model of innovation illustrated above is also different from the linear model of innovation which assumes a logi-cal and temporal sequence among basic research, applied research, development, production, marketing and diffusion. In particular, this traditional model leads to neglect of various important types of knowledge different from analytical or codified knowledge (Asheim et al., 2007), such as synthetic or engineering-based knowledge and symbolic or creativity-based knowledge, and also managerial and institutional knowledge or capabilities. Further shortcomings of the linear model are that it considers only the internal process of innovation within the individual firm or in-house R&D activity, rather than the case of interacting firms, and that it focuses on the process of knowledge transfer from research to innovation rather than on the knowledge generation process. In fact, while a linear approach seeks to promote transfers of information and modern technology or to provide customized expertise to individual firms, a systemic approach (Lundvall, 1992; Antonelli 1998 and 2005) focuses on promoting knowledge networks and cooperation among the various local and external firms and actors in the regional innovation system, and on development of their internal capabilities. This cognitive model also differs from the “chain linked model” (Kline, Rosenberg, 1986), which envisages a tight rela-tion or feedback within an individual firm between production activities and those of commercialization and research. On the contrary, the cognitive model highlights the interaction among different firms and actors, and it is systemic in nature.

Finally, the cognitive model seems appropriate to the explanation of innovation in SMEs operating in medium-technology sectors and in service activities, but it may also prove useful in highlighting some characteristics of R&D activities. Cognitive theories which focus on the knowledge generation process explain that knowledge and innovation result from an interactive learning process occurring in a network made up of various actors, and they enable the identification of different phases or factors in this process. In particular, a systemic or cognitive model underlines the

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importance for innovation of three general concepts: connectivity, creativity, and speed of change (Cappellin, 2003a, 2009; Cappellin, Wink, 2009) which also apply to high-technology sectors. This model of innovation thus highlights the close technological interdependence between medium-technology industrial sectors and high-technology industrial ones. On the other hand, the knowledge creation proc-ess has recently changed even in high-technology sectors. In fact, from a cognitive perspective, R&D activity should not be treated as a black box transforming inputs into outputs or R&D funds into patents and publications. In particular, the tacit knowledge of individual researchers, interactive learning within research teams, networks of extensive and systematic international cooperative relationships, and concepts such as trust, identity, leadership and social capital, seem to be key charac-teristics also of scientific communities and knowledge organizations like scientific associations and journals, and of R&D activities within universities and firms.

In conclusion, now necessary is a radical shift of perspective away from a tradi-tional paradigm based on the concepts of technologies, R&D expenditure, rational optimization processes of individual firms, market competition among firms, and the resistance or receptivity of labour to the new technologies. On the contrary, innovation processes can be interpreted according to a new paradigm focused on knowledge creation processes, interactive learning, iterative adaptation and selec-tion within innovation networks, and development of the internal creativity and entrepreneurial capabilities of firms and actors.

6. The Spatial Dimension of the Learning Process and Territorial Knowledge Management

Clearly, space and territory matter in the processes of cognition and the generation of knowledge. Cognitive processes have a localized dimension, and the innovation process has a “territorial embeddedness” which favours the spatial agglomeration of innovative activities. From this perspective, it may be useful to draw a distinc-tion related to the three well-known concepts of “polarised region”, “homogenous region” and “planning region”, which respectively focus on the concepts of tight flows, place identity, and common institutions. First, with regard to the concept of “polarised region”, if interactive learning is the key process in knowledge creation, then it is clear that the links and the frequency of the contacts among the nodes of the network are constrained by spatial and/or cognitive distance.

Second, with regard to the concept of “homogenous region”, knowledge results not only from the combination of a new stimulus with the individual previous expe-rience which characterizes the personal identity, but also from the combination of different competencies owned by the various actors interacting in a learning proc-ess occurring within a given geographical and sectoral cluster or network, which has a collective identity. Thus, from a spatial perspective, the same stimulus may determine a different response pattern in each regional innovation system accord-ing to the characteristics of the network of local actors. Regions are characterized


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by different place identities and by differences in the relationships of homogeneity or internal diversity and complementarity among the local actors. That may gener-ate trust, common identity and sense of place belonging, and it may facilitate or hinder innovation.

Finally, with regard to the concept of “planning region”, success in solving previous problems strengthens particular links among some local specific actors and creates soft infrastructures, such as routines, norms, organizations, interme-diate institutions and public institutions, which will facilitate future interactions among these same actors in the region considered. Therefore, the policy networks of regional actors and the institutional thickness of a region may accelerate the speed of innovation.

Moreover, territorial networks may be classified into three types: “ecological networks”, “identity networks” and “strategic networks”. These have different characteristics (Cappellin, 2003b, 2007). ‘Ecology networks’, such as “Third Italy” industrial districts in the late 1960s and early 1970s, are characterised by strong unintended interactions among various actors, and they facilitate various forms of traded and un-traded technological interdependencies or technology spillovers such as those which occur in geographical agglomerations. ‘Identity networks’, for instance “Third Italy” industrial districts in the late 1990s, are based on the sense of identity and common belonging and on the existence of trust relationships and specialised intermediate institutions (“social capital”). ‘Strategy networks’, such as metropolitan areas and some industrial clusters in various European countries during the 2000s, are based on intended relationships and cooperative agreements between firms and other organisations. This typology of regions differs in some respects from similar typologies (Gordon, MacCan, 2000; Cooke et al., 2003; Asheim, Coenen, 2005; Tödtling, Trippl, 2005).

Therefore, regional production systems may evolve from simple agglomera-tions of similar SMEs, as in so-called “ecological networks”, into communities characterized by intense processes of interactive learning, as in so-called “identity networks”. They may finally evolve into “strategy networks” characterized by the explicit governance of knowledge interactions among the various firms. In particu-lar, the six phases of the knowledge creation process and of interactive learning illustrated above in the “Territorial Knowledge Management” approach make it possible to identify the objectives or priorities of innovation policies in different types of region.

The major weakness factors in “identity networks”, such as clusters special-ized in medium-tech sectors, seem to be: 1) a low international accessibility, 2) the relative lack of creativity and of major product innovation instead of the focus hitherto on process innovation in traditional productions, 3) the need for formal instruments of governance of knowledge relations in order to foster the emergence of more formal cooperation among firms. Moreover, innovation policies in modern industrial clusters specialized in medium technology sectors should also take account of the nature of their knowledge base, which mainly consists of synthetic

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and symbolic knowledge (Asheim, Boschma, Cooke, 2007), and the form of their knowledge interaction characterized by interactive learning processes.

On the other hand, clusters specialized in high-tech sectors exhibit various key problems, such as: 1) the low local embeddedness of large firms, 2) problems in combining R&D activities or analytical and synthetic knowledge, which are science and technology-driven, with symbolic knowledge, which is based on crea-tivity and driven by users’ needs and demand, 3) the need to avoid excessively high concentration in large firms and to promote spin-offs and participation in strategic decision making also by SMEs and other social partners. These clusters are mostly to be found in central and metropolitan urban areas. Knowledge networks in these areas are characterized by links between large firms and research institutions and by professional networks and knowledge-intensive business services. Innovation policies in these areas should take account of the nature of their knowledge base consisting of analytical and synthetic knowledge, and the form of the knowledge interaction characterized by knowledge flows coordinated by knowledge manage-ment and joint R&D projects.

Finally, clusters specialized in low-tech sectors are characterized by various weaknesses, such as: 1) too low international accessibility, 2) a lack of receptivity and qualified skills, 3) a lack of identity and fragmentation in decision-making. These clusters are typically located in less developed and peripheral areas. They have to date competed exclusively on cost advantages, and they are to a large extent dependent on public subsidies. Innovation policies in the less developed peripheral areas specialised in low-tech sectors should take account of the nature of their knowledge base, which consists largely of symbolic or creativity-based knowledge and sometimes synthetic or engineering-based knowledge, and the form of knowl-edge interaction in these regions characterized by automatic knowledge spillover based on geographical proximity.

Therefore, according to a cognitive perspective such as that adopted by the territorial knowledge management approach, innovation policies should not only consist in the financing of private R&D investment with public resources; they should also facilitate accessibility among actors, stimulate their internal capabili-ties, increase their receptivity, promote a sense of belonging to the same commu-nity and the identification of common aims, enhance relationships with different and complementary actors both regional and external for the creation of new firms and productions, and accelerate the sequential and cumulative process of trial and error among the innovations of different firms. For these reasons, cluster policies require new forms of regulation of relationships among local actors, and also the identification or creation of new organizations and institutions. The multiplication of players, and the management of knowledge relationships between them and the various layers of negotiation – international, national, and local (Cappellin, 1997, 2005) – demand a different model of regulation, called “multi-level governance”, based on organisational structures of interaction and partnership.


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7. Different Forms of Regulation of the Relationships among Economic Actors

The economic policy debate which has developed at the international level since the 2008-2009 crisis oscillates between two opposite models: the free-market or “laissez-faire” model and the government or central-planning model, with a rather confused search for a hybrid compromise between these two models according to the specific policy fields and countries considered. The negative effects of the free market model have been demonstrated by the collapse of the financial markets during the recent crisis. On the other hand, a return to the dirigiste model is clearly unfeasible. However, also the search for a compromise between the free market and the government model is misleading, given the characteristics of the modern economy in developed countries, which is no longer internally homogenous and made up of isolated individuals, as in the theoretical perfect competitive market, but highly differentiated and interdependent, as illustrated by the network model.

From a theoretical perspective, three different models of the regulation of economic actors are possible in a capitalist or market economy: the model of governance or negotiation, the free market or competitive model, and the govern-ment or top-down planning model (Cappellin, Wink, 2009). In the top-down plan-ning or “government” model (or economic planning), decisions are enforced on the basis of a principle of authority. This model applies not only to the national state or other public institutions but also to the internal organization of private firms. By contrast, the free market (or “laissez faire”) model is based on the principle of competition among an infinite number of firms which are all equal, and it implies conflict and “survival of the fittest”. This model claims that “the best policy is no policy” and that public intervention distorts the efficient allocation of resources automatically ensured by the market (Bianchi, 1995). Finally, the model of govern-ance (other synonymous or related concepts are polycentric society, collective bargaining, “neo-corporatism”, “concertation”, social partnership) is based on the principle of partnership among a limited number of actors which are different and complementary and reciprocally recognized and legitimized. It implies negotiations and agreements and also the existence of contracts, trust and leadership (Streeck, Schmitter, 1985; Powell, 1990; Keeble et al., 1999; Marsh, Smith, 2000; Pierre, 2000; European Commission, 2001; Nooteboom, 2002; Antonelli, 2005; Kaiser, Prange, 2004; Rhodes, 2007; Dahlstedt, 2009).

These three different policy-making models focus on three different instruments for the organization of economic relations between two actors: the mechanism of regulations and top-down coordination in the hierarchical model, the mechanism of prices in the free market model, and the mechanism of contracts, bargaining and leadership in the governance model. The differences among these three forms of organization and regulation of economic relationships are summarized in table 1.

Different behavioural mechanisms and motivations characterize the three models of regulation: orders, control and respect for authority and adaptation characterize the hierarchical government model; freedom, competition and also conflict and exit

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Table 1 - Forms of Organization and Regulation of Economic Relationships

Government Free market Governance

1. Principle Authority Competition Negotiation

2. Result aimed Order Equilibrium Partnership

3. Information provided Regulations Prices Contracts

4. Instruments of organization Control and adaptation Price taking Bargaining and leadership

5. Individual motivation and behaviour

Respect for authority Autonomy, exit or conflict

Trust and respect for agreements

6. Complexity Hierarchy Individualism Interdependence

7. Efficiency factors Economies of scale Perfect mobility and flexibility

Transaction costs and adjustment costs

8. Interdependence Vertical integration No external economies External economies

9. Number of actors Individual actor Infinite number Limited number

10. Level of integration Maximum integration Minimum integration Intermediate integration

11. Field of action Sectors Markets Policy networks

12. Problems addressed Authoritarianism Monopoly and

price collusionConflicts of interest and

lock-in effects13. Corrections to

problems Democracy Antitrust policy Specialization and dynamic coordination

14. Political Ideal Egalité Liberté Fraternité

15. Juridical base Civil law Common law Selfregulation and subsidiarity

16. Space of relevance State and Corporations Liberal Market

EconomiesCoordinated Market

Economies

17. Goods Scale intensive goods Commodities Specialized goods

18. Competitiveness factor Economies of scale Lower prices Time advantage

19. Type of innovation Radical innovation Incremental innovation Systemic innovation

20. Knowledge base Basic research Codified knowledge Tacit knowledge

21. Time framework Static Static Dynamic


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characterize the free market model; and trust and respect for agreements character-ize the governance model.

In synthesis, we may define governance as a model which regulates the rela-tionships among the firms and actors belonging to a network on the basis of inter-dependent adjustments decided through negotiation procedures (Cappellin, Wink, 2009). Governance refers to the non-hierarchical model of governing characterized by the involvement of non-state actors in the formulation, decision making and implementation of public policies (Kaiser, Prange, 2004). Governance addresses the need to manage the interdependent activities of a variety of actors vertically across different territorial levels as well as horizontally across different decision-making arenas (Héritier, 2002).

Multi-level governance depends on the existence of complex policy networks between private and public actors and also on the interdependence among different levels: regional, national and European. Moreover, free competition and free trade may be appropriate at the international level. Planning requires powerful and effi-cient institutions at the national level. However, the tight interdependence among a limited number of different and complementary actors at the regional level requires governance of their respective connections especially in the case of major projects for medium-term development and innovation.

The state’s role in a governance approach is not that of imposing solutions, but rather of facilitating them according to a “transactive” approach. This is different from a “prescriptive” approach because the most important problem is not “what to do” but rather how to promote connectivity, creativity and speed of change, or “how to do” and “with whom” (Cappellin, 1997). In fact, “good” leadership in good governance is the ability to steer the action of other actors, and it is more an art than a form of codified knowledge.

In synthesis, the governance and negotiation method appears indispensable in those complex cases where the state must intervene “ex ante” in the difficult search for consensus among the various bearers of rightful but conflicting interests. Other-wise, when the state had not intervened before and it had adopted the policy of the “laissez faire” or of free competition, the state would be obliged to intervene “ex post” through judicial power to solve the inevitable legal conflicts, occurring among those same “stakeholders”.

To be underlined is that the governance model of regulation is not constrained to a so-called “third sector” of the economy consisting of non-profit and non-govern-mental organizations and distinct from the public and private sectors (Powell, 1990; Cooke, Morgan, 1998); nor is it related to the legal, public or private, status of the actors considered. Rather, as indicated by the various characteristics presented in table 1, governance is a well-defined form of regulation based on a principle – negotiation – different from other principles such as authority or competition (Cappellin, 1997). Thus, the governance model has a more general relevance than the so-called “third sector”, and it may apply to all relationships in a modern market or capitalist economy, such as the complex and interdependent relationships among the public sector, private companies and non-profit and non-governmental

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organizations. Moreover, the governance model can be applied to regulate part-nership relationships within the public sector, such as those among the various national and regional public institutions. The governance model also applies to the organization of relationships within the private sector, as in the case of so-called “corporate governance”, and in the management of the various forms of coopera-tion among different private organizations: industrial companies, banks and busi-ness services.

On the other hand, each actor, whether a public institution or a private firm or a non-profit and non-governmental organization, may itself apply each of these different principles in its relationships with other actors. For example, a firm usually applies a hierarchical principle in the management of its internal relationships. Or it may follow a competition principle when dealing with similar and competing insti-tutions, firms or actors. Finally a negotiation principle may be adopted by a private firm or a public institution when dealing with other complementary and cooper-ating firms and institutions. Similarly, also the relationships among regions and countries may be characterized by one of the three types of relationship: hierarchy, competition and bargaining. Finally, regional public institutions may adopt each of these three forms of political regulation within their various activities according to the different policy fields considered.

Especially in a modern knowledge and capitalist economy, governance is a very important form of regulation because specialization and interdependence are key characteristics of the various actors. Clearly, governance is more relevant in the case of some economic sectors or of specific type of relationships. For example, it may be more important in regulation of the complex relationships within the firm networks of medium-technology sectors than it is in the case of large capital-intensive industries. It may be more important in the case of knowledge and infor-mation relationships than in the case of the exchange of material products, where a competitive or hierarchical principle may be more common or suitable. It may be more important in the exchange and sharing of information and knowledge and in decisions concerning a medium-term or strategic perspective than in the every-day market and material exchanges decided on the basis of price competitiveness. Actors must therefore evaluate which is the most suitable form of regulation of their reciprocal relationships.

While most of the political science literature compares the governance and government models (Boyer, 1990; Marsh, Smith, 2000; Pierre 2000; Powell, 1990; Rhodes, 2007; Streeck, Schmitter, 1985), an economic perspective shifts the focus to the problem of the respective advantages of the governance model and the free-market model in the regulation of economic relationships in a modern capital-ist system: an issue which characterizes the current debate on privatization and marketization.

Unlike the other regulation models, according to a market model the actors refuse to obey and also to agree, and they prefer to compete against each other. The actors adjust their willingness to supply and demand goods or services only in response to the price signals generated by markets. Markets are self regulating, and


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economic relationships are indirectly or automatically coordinated by the market, which assigns productions to the most competitive firms as the result of competition among the many existing suppliers and of the optimal choice by the many possible users. In a governance model by contrast, , coordination is the result of negotiations and explicit agreements among a limited number of individual stakeholders.

The free-market model is certainly inappropriate in a modern knowledge econ-omy, where information and knowledge are the most important production factors, and sometimes even the final output to be created and used by consumers. In fact, asymmetric information prevails and tacit knowledge and personal competencies can hardly circulate on conventional markets. Hence exchanges require intermedi-ate institutions or private and public organizations able to enhance reciprocal trust, the sense of identity and the development of shared values and collaboration, while limiting opportunistic behaviour, adverse selection and moral hazard.

Differently from the free-market and perfect competition models, where infor-mation is available to everybody and individual actors have no effect on prices and behave in non opportunistic manner, the most modern sectors of the economy are characterized by external economies and asymmetric information. The competi-tive relationships among the actors are no more important than the pervasive relationships of complementarity and interdependence among those same actors. This creates a need for coordination among these actors based on the attainment of common aims which may yield improvements for each individual actor (“Pareto’s optimum”)

The governance model is a change from the free market model, and it seemingly corresponds to a new phase of development of the capitalist economy in which technology is increasingly systemic because innovation in a single firm depends on innovation in other firms, and the speed of innovation has become more crucial than reducing production costs to compete with other firms. These changes require the closer integration of the various actors and the emergence of networks among them.

A neo-liberal model constantly advocates greater competition, greater price and wage flexibility, and greater labour mobility as the panacea for every economic problem in labour markets, financial markets, the restructuring of industrial sectors, university and research organizations, and even relationships among the regions of the same country. However, in a modern knowledge economy the concept of innovation seems more important than that of price and wage flexibility, and the concept of integration of the various economic actors appears more crucial than that of further promoting the already high competition in national and international markets.

In fact, the increasingly wider adoption of a governance model results from adap-tation to a continuously changing environment, rather than being a deliberate change of strategy. It is embedded in the ongoing structural dynamics largely common to all European countries. In particular, it is now widely recognized that the interven-tionist top-down model (“government”) in innovation policies is neither possible nor desirable, since innovation by its very nature cannot be reduced to command:

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it has a proactive character and is open to new discoveries. Moreover, the dirigiste approach of economic planning and the “welfare state” should be changed into an approach based on the concept of partnership and subsidiarity.

The governance approach in policy-making is closely related to innovation, because this latter erodes the disciplinary borders and internal hierarchies which characterize the government model. For example, Schumpeter’s creative destruc-tion clearly causes conflicts and does not respect consolidated hierarchies. Govern-ance is also closely related to the internationalization process, because this latter undermines closure and hierarchies and erodes the regulatory capacities of states. Moreover, the internationalization of economies ensures that innovators have free-dom of exit from hierarchical organizations when they cannot accept a dependent role.

In particular, governance is the most suitable regulation method in the case of complex relations such as those existing in the knowledge and innovation networks of the medium-technology sectors. These networks are made up of numerous actors, among which knowledge should be shared or exchanged. A dirigiste approach cannot be taken in these clusters owing to the lack of a superior authority. On the other hand, a free market model based on competition would weaken the trust rela-tionships required for the sharing of tacit knowledge among the local actors.

In conclusion, the “laissez-faire” or free-market approach, which has been responsible for the recent financial crisis in 2008-09, does not seem to be a model which enables to tackle the complex and strategic problems which characterize a modern capitalist system. This debate on the respective advantages of the govern-ance and the free-market models is related to the debate on the respective advan-tages of the so-called liberal market economies (LMEs, such as the United States, the United Kingdom, Australia, Canada, New Zealand and Ireland) and the coor-dinated market economies (CMEs, such as Germany, Austria, Switzerland, France, Italy and also Japan) analysed by Hall and Soskice in their edited volume on “Varie-ties of Capitalism” (2001 and 2003). In fact, the management of interdependencies among individual, collective and corporate actors in coordinated market economies is different from the government model (“dirigisme”) and also from the market model (“neo-liberalism”).

In these economies, actors are entitled to regulate autonomously important aspects of sectoral and economic development according to principles of verti-cal and horizontal subsidiarity (Lehmbruch, 1977; Cappellin, 1997), and strate-gic interaction or non-market relationships among firms and other actors have a key role in investment decisions and innovation. In the highly-coordinated market economies of continental Europe, intermediary organizations play an important role in the processes of exchange among economic actors. In some cases (i.e. technical standardization), communities of individual actors may establish “private interest governments” through their respective organizations (Streeck, Schmitter, 1985). Sorge and Streeck (1998), for example, have identified this influence as the main reason why German industry has achieved a comparative advantage in the field of “diversified quality productions”.


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The governance model is clearly linked to the search for consensus which is a traditional characteristic of the European social model, as indicated by the neo-corporatist model (Lehmbruch, 1977; Schmitter, Lehmbruch, 1982; Streeck, Kenworthy, 2005) of tripartite collaboration in the labour market among trade unions, employers organizations, and the government. On the other hand, it also differs from the related concept of the “social market economy”, which instead advocates a strong role of the state in preventing the concentration of economic power in specific interest groups and in maintaining a so-called “achievement competition” (Friedrich, 1955).

In particular, specialization in medium-technology sectors organized into networks of SMEs is closely related to the existence of a complex system of inter-mediate institutions made up of local chambers of commerce, territorial and sector-specific industry associations, trade unions, professional associations, public voca-tional schools, local universities and research organizations, local banks, etc., and the adoption of the governance model in social and institutional relations. Related to this are the principles of vertical and horizontal subsidiarity which characterize various federalist or regionalized states of continental Europe.

Hall and Soskice hypothesize that, owing to their different form of regulation, liberal market economies (LMEs) specialize in radical innovation, while coordi-nated market economies (CMEs) focus more on incremental innovation. The argu-The argu-ment put forward here is similar, but also different because it maintains that the existence of a thick system of intermediate institutions plays a key role in explain-ing the concentration of medium-technology sectors in coordinated market econo-mies. By contrast, the lack of this system of intermediate institutions explains the absence of significant clusters in medium-technology sectors, as well as the large trade deficits in these sectors by liberal market economies such as the United States or the United Kingdom.

Massive public industrial investment subsidies, and also large public expendi-ture on R&D as in the United States after the 2008-2009 global crisis, may prove ineffective for the reindustrialization and the development of medium technology sectors in those countries which have long since lost production capacities in these sectors. In fact, required in this regard is a long-term effort to construct a system of intermediate institutions which are almost non-existent in those countries and represent the institutional environment without which it is impossible to promote the complex processes of innovation characterizing these sectors.

8. Levels of Integration, Governance and the Economy’s Speed of Change

Free market, governance and government are three different forms of regula-tion of economic relationships characterized by different levels of integration. The liberal free market approach, which implies atomistic or autonomous decisions by individual firms and the role of the market’s “invisible hand”, represents the lowest

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level of integration. By contrast, the hierarchy model, where the relationships among the actors are very tight and must comply with the instructions of a superior power, which may be the state or the managers in a large integrated company, represents the highest level of integration. Thus, the networks of firms highly specialized in different specific production phases represent an intermediate case of integration based on the principle of negotiation, consensus and cooperation.

In particular, innovation highlights the importance of the concept of time, as indicated by various other related concepts such as just in time in production, lead time in consumer response, rigidities, inertia, stickiness, time lags in the adop-tion of innovation, time to market, time advantage, speed in decision-making and coordination, and speed of change. Hence the relationship between the level of integration implicit in the three forms of regulation of economic actors indicated above, and the speed of change or innovation in a cluster or in an economic system can be indicated as in Figure 3.

In fact, too high competition among the local firms in a cluster hinders the possi-bility to combine their limited resources. Moreover, individual small firms may have internal creative capabilities. However, their creativity and speed of innova-tion may be reduced by the fact that they cannot find internally all the compe-tencies required to respond to an external stimulus. This induces small firms to create networks which may perform a key role in steering the evolution of the local clusters and in promoting change and a long-term strategy. A network organization gives firms easy access to rare complementary competencies owned by other local firms, thereby increasing their capability to respond to external stimuli, exploit external opportunities, and deal with external threats, leading to higher creativity and speed of change.

On the other hand, too high integration, as in a large firm or in hierarchically organised supply chains vertically integrated by a leader firm, may be less able than a network to exploit creativity potential. In fact, a large firm made up of disparate business units may be rather closed to external stimuli and external competencies. The outsourcing of non-core productions and a focus on those areas in which the firm enjoys a technological advantage may therefore be a more efficient strategy in order to avoid a lock-in effect. In fact, peripheral technologies for one firm may be core activities for another, and large firms have increasingly created financial participation in other firms, or flexible alliances or networks with other firms, in order to accelerate the speed of innovation.

A network may be a form of organization or a governance structure which is more effective in promoting creativity or knowledge creation than either a pure competitive market or a hierarchical organization. Creativity, continuous change, and innovation require interactive learning processes among many different actors, and cooperation among various firms is more efficient than the two extreme situ-ations of, on the one hand, the isolation of individual firms competing with each other, and on the other, the integration of all production into a large firm where the relationships among actors are regulated by a central authority (Cappellin, Wink, 2009).


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In conclusion, new institutional and organizational structures are needed in order to facilitate structural adjustment to a knowledge economy, enhance social interac-tions, and accelerate the speed of innovation adoption. The purpose of governance is to decrease the transaction costs (Williamson, 1981; Cappellin, 1988) among the various economic and social actors. Governance also plays a key role in determin-ing the flexibility of an innovation system and in reducing the “switching costs” or adjustment costs of innovation (Cappellin, 1983b). It promotes a higher speed of change, and it prevents the risk of a lock-in effect in territorial clusters by promot-ing a horizontal and vertical diversification of the traditional productions in clus-ters. The governance model therefore entails the existence of intermediary func-tions, a greater stability of relationships, a long-term perspective, and the supply of adequate public investments.

In particular, the governance of innovation networks makes it possible to tackle those problems which hinder the speed of innovation and lead to lock-in effects in modern capitalist economies: bottlenecks, missing links, inertia, resistances, corporate rigidities, collusion, exclusion, privileges and rents, and redistributive inequalities. It prevents fragmented decision-making and reduces organizational conflicts among the various actors.

The free-market model leads to competition in an horizontal perspective. However, it does not prevent forms of collusion and quasi-integration in a vertical perspective and among different sectors. In fact, in many of the modern Euro-pean capitalist economies, the various forms of collusion among large firms in

Figure 3 - The Relationship between Greater Integration and Faster Change

High

Atomistic competition Networks Hierarchy

0 Free market Governance Government 1 Levels of integration and forms of governance

Speed of change

Low

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the banking and insurance, industry and media sectors, through direct and indirect financial links and the exchange of positions among the boards of these organiza-tions, and the close personal relations among their representatives within industry associations and with the world of politics, generate pervasive conflicts of interest between the supplier and the user, between the controlled and the controller. They are also among the main reasons for increasing income disparities, and they give specific groups of actors an advantage over other groups. In fact, a market which operates freely without rules inevitably leads to collusion and the concentration of economic and financial power into the hands of a few actors.

Forms of intersectoral collusion represent a danger and create rent situations. Intersectoral integration may lead to conflicts of interests and endanger the “checks and balances” which are the basis of a polycentric society and a pluralistic democ-racy, as in Montesquieu’s principle of separation among the legislature, executive and judiciary powers. In fact, totalitarism occurs when all political and economic power is concentrated into a single group of actors or a ruling class. The more developed a society, the greater should be the “division of labour” or specialization among sectors, and also the division of powers among firms and organizations.

These collusions and conflicts of interest are aimed at short-term financial profits, and they defend and exploit specific rent positions. The cause of slow innovation is not the lack of competition or of competitors in the specific market considered, but rather the fact that the various markets are not isolated from each other. The firms in a specific market may choose not to compete with each other, and thus determine lower prices or better quality for customers, because they consider it more impor-tant to create other types of non-transparent benefits for third actors which control them and which prefer to maintain an oligopolistic equilibrium. These conflicts of interest represent the major obstacle against systemic or inter-sectoral innovation and diversification in European industry because new innovative initiatives could conflict with the incumbent organizations and could undermine the existing power alliances among them. Clearly, SMEs in medium-technology sectors are excluded from the exclusive financial networks among the large firms of different sectors, and they are hindered in their diversification and growth into new sectors.

These forms of intersectoral collusion and conflicts of interests are favoured by a free market approach or by the lack of regulation and they cannot be tackled by the traditional competition policies. Instead, they require governance of the relation-ships among the various economic actors. In fact, the governance model enhances the combination of complementary capabilities on the basis of the recognition by each actor of each other actor’s legitimacy, and public and transparent negotia-tions and agreements. In the governance model, too, the relationships among actors are based on monetary or real exchange. But they are the result of negotiations and agreements, not of the automatic workings of the market. This contrasts with the free market model, where competition generates conflicts, and firms endeav-our to defeat and exclude their competitors. It also contrasts with the hierarchical relationships based on vertical integration, mergers or collusion characterizing the planning model. In particular, each node in a network should perform a different


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function or role. Thus the governance model leads to regulations which promote the specialization and separation of the fields of activity of firms and organizations. It seeks to prevent the collusions and conflicts of interest which may damage other actors, and to guarantee a system of checks and balances.

In conclusion, each regulation model of economic and social relationships may lead to problematic situations and requires adequate instruments to correct them. Democracy avoids the problem of totalitarism in the government model. Compe-tition or anti trust policy is required to prevent collusion and monopolies in the free market model. Governance avoids the problem of intersectoral collusion and conflicts of interest in the case of networks.

The governance model promotes integration among the various autonomous economic and institutional actors, and it enhances the development of both market relationships and a pluralistic democracy. The procedures of negotiation in a govern-ance model link the major economic and institutional actors together through an interactive and sequential learning process. Both market and hierarchies continue to exist, of course, but they operate with in the framework of decision processes having a negotiation nature.

9. “Competence Centres” and the Governance of Innovation Networks

The aims of an European innovation policy are to increase overall productivity, to promote the greater competitiveness of exports to non-European countries, and to facilitate a fast transition to a modern knowledge economy. A policy for the knowl-edge economy based on the “governance” approach entails so-called “dynamic coordination” or the use of policy instruments different from those usually adopted in traditional innovation policies, such as public R&D, public subsidies to private R&D, public demand for innovative products and services, and IPR in order to ensure that innovators have a time-limited monopoly power.

In particular, empirical and theoretical research on innovation in medium-tech-nology sectors (Cappellin, Wink, 2009) highlights the need for regional innovation policies to evolve:

from the traditional free-market approach or the hierarchical planning approach a. to a modern governance approach, from the focus on individual firms to the governance of the firms network,b. from the distribution of R&D public funds to the connection of innovative c. capabilities,from a focus on exploitation of specific technologies to one on exploration of d. diverse technologies,from sectoral specialization to intersectoral integration and sectoral e. diversification,from a focus on process innovation and cost competition to one on product inno-f. vation and time competition,

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from a focus on accessibility to technological sources to one of receptivity by g. the local actors,from the supply R&D infrastructures to the identification of new demands by h. final and intermediate users,from public finance of R&D and public regulation of markets to multi-level i. governance, creation of bridging institutions, and enhancement of public/private partnerships,from informal cooperation based on trust to formal commitment to strategic j. projects.The governance approach entails coordinated action to achieve common aims

using dedicated resources from the various partners. It may give rise to a differ-entiated set of “intermediate” or “bridging” institutions able to design and organ-ize strategic joint actions, for example: specialized schools, international calls?, joint industrial projects, strategic planning contracts with large firms, cooperative research projects among SMEs, regional innovative start-up funds, joint R&D projects, non-governmental research institutions or foundations, regional techno-logical parks and centres, local stakeholder coordination committees, territorial pacts with local actors, regional innovation strategies, national programmes for R&D and innovation networks, territorial knowledge management, networks of research centres of excellence, and regional and national networks of competence centres. These diverse institutions also represent the social capital of regions, and they act as immaterial infrastructures which organize the knowledge flows among the various regional actors, particularly in the case of SMEs specialized in the medium-technology sectors.

In particular, national and regional competence centres are designed to stimulate cooperation on research and technological development in strategically important production fields among companies, academia, the public sector and other organi-sations involved in promoting innovation, thereby narrowing the gap between pre-competitive technological research and practical industrial application. Competence centres are new instruments of innovation policy suitable for SMEs in medium-techlogy sectors. The experience of some countries in which national or regional networks of competence centres have been created in the past few years, such as France, Finland, Austria and some Italian regions, could be extended to other European countries and regions which still lack an explicit national or regional programme for the creation and management of a national network of competence centres. The results obtained so far vary according to the specific sector and region considered and they are encouraging, although competence centres are not the only instrument in innovation policy and, clearly, can be combined with other forms of multilevel governance, as indicated above.

The idea of cluster policies and competence centres in various European coun-tries is based on the following characteristics of competence centres. They:

are part of a national or regional network created by a national or regional public a. programme which has defined a competitive mechanism for selection among the


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various proposals of competence centres and a national or regional agency to steer the overall network of competence centres,have a regional focus but act on an international scale, b. concentrate on a specific thematic production field, c. are able to generate innovations with a particularly high value-added potential, d. cover many links in the value chain and connect multiple sectors of industry and e. scientific disciplines, establish an outstanding communication and cooperation platform by promoting f. public/private partnerships and existing networks between large and small firms and other regional actors, in close cooperation with universities and research, educational and vocational centres, aim to implement a common strategy of innovation and economic development g. for a specific territorial cluster or regional innovation system, represent an innovative and operational mode of “governance” or a “soft infra-h. structure” that aims to develop synergies around specific collective innovation projects oriented to one or more well-focused markets,allow achievement of a critical mass in order to develop international visibil-i. ity in an industrial and/or technological perspective and to increase a cluster’s attractiveness with respect to international competitors.Competence centres differ from research “Centres of excellence”, which consist

mostly in large research institutions focused on well-defined fields of advanced pre-competitive research, often in close cooperation with specific industries. Their purpose is to raise the quality of research and to improve its international visibil-ity and reputation. In fact, competence centres aim to promote the accumulation of knowledge among different firms and sectors through processes of interactive learning, rather than focusing only on investment in R&D, because they attribute a key role to exchanges of tacit knowledge and to the building of specialized competencies.

Competence centres also differ from the traditional “technological centres” created by local and regional institutions with the purpose of providing new tech-nological and business services to individual SMEs within territorial clusters. On the contrary, competence centres engage with several firms and other partners in the design and management of large joint projects for the development of innova-tive productions for the industrial diversification of a cluster.

While large and medium-sized firms have developed vertical flows of tacit knowledge within their respective supply chains, they need support in develop-ing horizontal linkages among different technologies and in promoting the sectoral diversification of the respective regional production systems by developing new productions in different sectors. Competence centres are crucial in reducing the “switching costs” related to innovation and in accelerating the adoption of innova-tion, thereby averting the risk of a lock-in effect in territorial clusters and diversify-ing the traditional productions in these clusters. Competence centres can carry out exploratory activities leading to the design of many industrial projects.

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Regional policy should identify production fields for the competence centres and the relevant target areas of new technologies to be developed. The follow-ing three fields may be considered by competence centres according to the stage of development of the respective region: a) developed fields of competence well connected with the current specializations of the regional economy, b) developing competence fields where strength in the supply by regional research institutions does not correspond to the actual demand by the regional firms, c) new emerging fields at an early stage of research which are in need of policy support for future development.

The choice of the specific sectors of activity in the competence sectors can be based on autonomous proposals by the various regional actors, and the selection among these proposals can be guided by the strategic competitiveness factors of the European economy with respect to the many and large emerging economies. These advantages are related to: a) a highly-skilled labour force with high educa-tion levels, b) the wide diversification of industrial productions allowing the crea-tion of new productions through the combination of traditional specializations in the various European industrial clusters, c) the complexity of the forms of coop-eration among the firms in the same sector, and also in different sectors, allowing the production of complex products consisting, not in individual machines, but in complete production systems which cannot be easily copied by individual firms in less developed countries, and d) the emergence of new needs among consumers and citizens (especially in the large European urban areas) which are often collec-tive in nature – such as health, environment, energy, culture and leisure – and which may be the drivers of new markets and promote the development of new sectors and new firms.

In particular, creativity consists not only in the adoption of specific product and process innovation within an individual firm but also in the design of medium-term projects of a collective nature with the participation of various SMEs and large firms organized in competence centres (Cappellin, Wink, 2009), as highlighted by the experience of various European countries. The enhancement of creativity requires facilitation of the vertical relationships along the supply chain between client and suppliers, but also of the horizontal relationships among different sectors both locally and with partners in other regions, such as other clusters, international research institutions, and large international firms. In particular, competence centres should carry out exploratory activity with a view to the design of many large and small projects.

Competence centres contribute to developing a new vision and long-term strat-egy. They increase awareness of the changes needed in clusters, and they stimulate firms and other actors in the clusters to innovate. Regional competence centres focus on new fields of production related to traditional specializations in the vari-ous regions, and they may promote collaboration among firms in different sectors with complementary competencies.

Competence centres may stimulate firms to change their corporate strategies by adopting a forward-looking perspective, and they stimulate the international


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openness of regional clusters by promoting forms of collaboration with external partners such as international research institutions and large international firms. The openness of competence centres to new actors is a decisive prerequisite for sustainability, and for the prevention of path-dependencies and lock-in effects or the emergence of an elitist club consisting of a few firms isolated from the rest of the cluster.

In fact, competence centres should not only focus on the needs of individual large companies or on their supply chains. They should also adopt a territorial perspective by dealing with horizontal relations among different sectors, and an institutional perspective by promoting new forms of multilevel governance. They should identify emerging needs in existing and new markets and create the coali-tions of regional and also international partners needed to solve problems.

Competence centres may be organized as public/private partnerships, of which the regional government acts as a promoter together with a consortium of private actors, while the regional business promotion agency acts as a supporting and managing institution. For example, various Western countries have promoted different forms of partnership between the state and private banks within the frame-work of the economy’s stimulus packages. The purpose of these partnerships is to create or to revitalize financial institutions and funds, as in the cases of the KfW in Germany, the Oseo in France, or the TARP in the United States, which may support innovation projects also with the participation of SMEs.

A systemic approach to innovation focusing on knowledge creation, interactive learning and the development of creative capabilities of regional firms enhances the identification of a more complex set of actions in innovation policies, rather than the financing of individual R&D projects. Hence regional and national policies for competence centres should promote:

a change from the focus on individual firms to the governance of the network a. of firms, a change from strengthening sectoral specialization to promoting intersectoral b. integration and sectoral diversification,a change from informal cooperation based on trust to strategic projects based on c. formal commitment, a change from the supply of R&D infrastructures to the response to emerging d. needs of final and intermediate users by identifying and aggregating new scat-tered demand, and discovering new markets with high growth potential or new “lead markets” for the regional productions,the use of the knowledge accumulated within the cluster, the circulation of tacit e. knowledge and the development of new competencies through interactive learn-ing among the local actors, new activities or “strategic spin-offs” which can lead the regional economy to f. diversify production into new sectors of application by investing in projects close to commercialization, rather than in basic research,

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the design and adoption of new large strategic projects of innovation requiring g. the coordination and cooperation of many partners in the existing clusters and regions, rather than the creation of new clusters,new funding through public/private partnerships involving modern financial h. intermediaries in strategic industrial projects and providing key competence to these institutions in the selection of the most innovative projects submitted, because the problem is the abundance of funding in the international markets and the lack of profitable projects at the local level,new formal and informal institutions, infrastructures, norms, rules and routines, i. adopting new forms of “governance” of knowledge and innovation networks and designing an explicit long-term strategy for the individual competence centres, the participation of new partners in innovation networks, such as KIBS and uni-j. versities, thus promoting greater commitment to innovation and a medium-term development strategy, local contacts among SMEs and large firms, on the one hand, and between them k. and the research institutions on the other, as competence centres act as bridging institutions,international links among competence centres in different countries, participa-l. tion in European projects, enhancing the cluster’s international integration and competitiveness in an increasingly complex and interconnected world.

10. Conclusions

This article has investigated the driving forces behind the process of interactive learning; the creation of knowledge and innovation; the importance of the govern-ance model with respect to the free market or government models in the regulation of knowledge and innovation networks; and the development of new policy tools, such as centres of competence in innovation policies for the management of coop-eration among regional and external actors in sectoral and regional clusters.

Innovation studies are often biased by a focus on high technology sectors, and they emphasise the need to diffuse science-driven, often formal and analytical, knowledge among research institutes, high-technology start-up and spin-off firms and multinational enterprises. But medium-technology sectors are complex and are still the growth engines of many industrial economies, such as those in the Euro-pean Union and Japan. Moreover, many important interdependencies exist between medium- and high-technology sectors.

The innovation process in the medium-technology sectors is different from that indicated by the linear model, which is focused on R&D expenditure and on the rational process of optimization within the individual firms, and aims to promote the transfer of information and modern technologies or specialized competencies to individual firms. By contrast, this article has illustrated a systemic and cogni-tive approach focused on the various factors which promote interactive learning in a modern knowledge economy, and it has illustrated the characteristics of the


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governance model in policies to organize knowledge and innovation networks among the various local, national and international actors in regional or sectoral clusters.

In particular, this article has first summarized the main characteristics of the “territorial knowledge management” approach (Cappellin, 2003a, 2003b, 2007; Cappellin, Wink, 2009), which identifies a logical and temporal sequence of six driving factors or phases in the process of interactive learning, knowledge creation and innovation: external stimulus, accessibility, receptivity, identity, creativity and governance. These factors, which have been studied individually by an extensive and interdisciplinary literature, are logically and temporally linked together in a process of innovation which corresponds to the recent findings in cognitive science. These factors also differ from the phases indicated by the traditional linear model of innovation, and from the factors of competitiveness and productivity change in Porter’s cluster model.

The learning networks approach underlines that time is the key dimension of innovation. The competitiveness of firms in regional innovation systems requires a faster speed of the process of change than in competing firms and regions. Well-structured production and innovation networks reduce transaction costs and adjust-ment costs, thus accelerating the change process, streamlining the policy-making decision process, and reducing implementation times.

This new approach therefore highlights a new role for public institutions in inno-vation networks, in that they should enable the six factors indicated above and foster appropriate behaviour by the various actors in a process of interactive learning and knowledge creation, rather than dictating specific solutions and productions. These factors also suggest that different policy guidelines and priorities should be adopted when promoting innovation in various types of regions.

The article has shown that the characteristics of these six factors or phases are incompatible with the hierarchical principle of the “government model” whereby innovative productions can be imposed top-down on firms or may result from R&D activities efficiently organized by individual firms or research organizations, because the interactive nature of the learning process requires collaboration with many external actors.

Moreover, the collaborative nature of the interactive learning process is incom-patible with the principle of competition inherent in the “free market model” which promotes conflicts among firms or local actors and a selection process whereby the most efficient producer defeats the less efficient producers and induces them to exit from the market. A new solution will not automatically emerge from the price and competition mechanism. This latter is efficient in transmitting price signals but inadequate in indicating the various, complementary and complex dimensions of a transaction, which requires direct negotiations and agreements between two or more specific actors.

The article has shown that “multi-level governance” is neither a residual third sector of a modern market economy nor a hybrid compromise between the tradi-tional forms of regulation: market and state. Rather, it is a third distinct model of

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regulation which differs from the other two models in various respects. Governance is an approach to industrial policy which is better suited to steering or manag-ing a modern capitalist system and the knowledge and innovation networks which characterize it. The governance or partnership model is based on the principles of negotiation, exchange and consensus. These differ from the principle of authority in the planning model, and from the principle of competition and survival of the fittest in the free-market model.

Finally, the article has illustrated some key characteristics of competence centres. These are new instruments for innovation policies and they may facilitate interac-tive learning among the local and external firms and actors in a regional economy, in that they enhance the various factors indicated above in a cognitive approach to the innovation process.

The structure of world and European industry will most probably be very differ-ent after the financial and economic global crisis of 2008-2009. The crisis compels accelerating the pace of innovation, dismantling conservative coalitions, promot-ing the diversification of productions and markets, and innovating the governance model in relationships among SMEs, large firms, financial institutions, private knowledge-intensive business services (KIBS), and research centres and public institutions.

The transition to the model of the knowledge economy requires a distinct change in industrial development strategies and in the approach to innovation policies at national and regional level. The focus should be more on knowledge creation than on technology diffusion, more on networks than on individual firms, and more on a European perspective than a local one.

Public institutions and innovation policies should extend the time horizon of economic calculations by private actors, improve their expectations, and induce them to increase their risk and investment propensity by promoting large-scale strategic projects for innovation and medium- and long-term investment using the negotiation or governance method, by assuring the requisite financial resources, and by arranging adequate organizational instruments such as a national network of competence centres.

The lack of public guidance and negotiations explains the slow processes of inno-vation. It provokes inertia; it hinders timely conflict resolution, and it slows down the decision times also of private firms. It is the true cause of the low growth rate of the Italian and European economy. Only partnership, “concertation”, negotiation, consensus, specialization and integration among very different and complementary actors can ensure that the key factors of international competitiveness: innovation, speed of change and flexibility, are present in a modern economy.


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