DIMETIC MARCH 2009 Muge ozman Institut telecom telecom & management sudparis Inter-fırm networks...

DIMETIC MARCH 2009

Muge ozmanInstitut telecom

telecom & management sudparis

Inter-fırm networks and ınnovatıon

INTRODUCTION

COLLABORATIONWHY ? WHICH ?WITH WHOM ?

WHY NETWORK

S?

FIRM PERFORMANCE

NETWORK STRUCTURE

Networks are considered to be important mechanisms that change economicand social outcomes. Tremendous increase in theoretical and empiricalresearch on networks in the economics and management literature.

One of the fields in which network research has grown at an impressive rate isinnovation and technological change, as it is now accepted that innovationis most effectively undertaken as a collective process in which networks play acentral role.

Innovation is a complex process, which includes many actors and theirinteractions like universities, government agencies, public and private labs...

These interactions take place through networks. Firms are the main actors inthe process of innovation and their interactions with each other shapeinnovative activities through knowledge diffusion and learning.

Research is highly interdisciplinary: physics, management, economics oftechnological change, geography, sociology...

INTRODUCTION


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In this lecture, we make an overview of research on firm networks and whatthey imply for innovation and technological change, the theoretical andempirical results obtained up to now in the field. What are the possible futuredirections for research?

For assesment of various databases, and guidelines for empirical research on networks: Understanding the alliance data Melissa A. Schilling * 2008, Strategic Management Journal

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The Growth of Network Research –

Social Sciences and Arts and Humanities Index, Number of Publications with “Network” in the title or abstract.

INTRODUCTION


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Source: F. Deroian, Z. M’Chirgui and C. Milelli, Evidences on inter-Firm R&D partnerships in threehigh-tech industries, GREQAM WorkinG Paper, 2007

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Think of inter firm relations, they can be in the form of informal relations, mergers, acquisitions, R&D alliances, know-how trading, licensing, franchising, or other types of interaction in a local or global context...

All these interactions have effect on diffusion of knowledge and innovation

business groups (Granovetter, 1998) which refer to the group of firms that are bound together in some formal or informal way, which is neither complete (like the case where firms consolidated into a legal single entity) nor weak (like in short term strategic alliances); the keiretsu in Japan, chaebol in Korea are the most prominent examples. Powell et al. (1996) use the term "networks of learning", where their emphasis is on the way networks facilitate organizational learning and act as the locus of innovation. Ring and Van De Van (1994) and Oliver (1990) use the term "cooperative inter-organizational relationships". Many other terms have also been used, including "networks of innovators" (DeBresson and Amesse, 1991), "network organization" (Miles and Snow, 1986), "strategic network" (Jarillo, 1988) and "interfirm networks" (Grandori and Soda, 1995). A somewhat more general term that incorporates not only firms but also other actors like public laboratories, research centers and regulatory bodies has been used by Callon (1998) in his term of "techno-economic networks".

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Origins of NetworksWhy firms collaborate?Which firms collaborate?

With whom do they collaborate?

Network structure

Architecture of networks, stability, efficiency

Firm performanceCapabilities, innovation, etc.

External conditions

ProximityKnowledge base

Complexityuncertainty

Effect of network structure on performance

Effect of firm characteristics on formation

Effect of network

structure on formation

Models of network

evolution

Network Structure

INTRODUCTION

COLLABORATION

WHY ? WHICH ?WITH WHOM ?

WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Why do firms collaborate? Which firms Collaborate?

With Whom do they collaborate?

Firm-Specific effects and industry characteristics, why, which?

1. Resource int.

2. Organizational learning.

Effect of social networks, with whom?

1. Embeddedness

2. Imitation

INTRODUCTION

COLLABORATION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Oliver (1990):a) necessity in the sense of meeting legal or regularity

requirements,

b)asymmetry potential of an organization to exercise power or

control over another organization,

c) reciprocity referring to the collaboration, cooperation rather than

the exercise of power (horizontal linkages rather than vertical)

d) efficiencyincreasing the internal input-output ratio of the

organization,

e) stabilityas an adaptive response to environmental

uncertainties,

f) legitimacyto improve reputation, image, prestige.


INTRODUCTION

COLLABORATION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

The earliest and the most widespread approach (Wernerfelt, 1984). Explains collaborations among firms with respect to the complementarities in firm resources.

The firm is a bundle of resources and the most common motive for collaborative relations is the interdependence in resources.

This means that firms form alliances with other firms because they are not self-sufficient, and they collaborate to reduce uncertainty as well as to access each others resources (Pfeffer and Salancik, 1978)

This is the case when: complexity and interrelatedness is high (Hagedoorn, 93), access to new markets, reduction in innovation period.

Interdependence in Resources


INTRODUCTION

COLLABORATION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Wang and Zajac (2007) Alliance or acquisition? a dyadic perspective on interfirm resource combinations, SMJ

Eisenhardt and Schoonhaven (1996) biotech: firms in vulnerable conditions (emergent, growing)

Miotti and Sachwald (2003) : resource dependence when there are many actors like gov labs, universities etc.

Das and Teng (2000) a review of resource based lit

Mesquita, Anand, Brush (2008) resource based perspective n explaining performance of alliances

Arora and Gambardella, 1994 biotech: scientific capabilities (reduce) and technological capabilities (increase). Eva Dantas and Martin Bell (2009) Latecomer firms and the emergence and development of knowledge networks: The case of Petrobras in Brazil Research Policy

Robin Cowan and Nicolas Jonard (2008) If the alliance fits …: Innovation and network dynamics



INTRODUCTION

COLLABORATION


WHY NETWORKS?

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Source: Hagedoorn (2002)

INTRODUCTION

COLLABORATION


WHY NETWORKS?

FIRM PERFORMANCE

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Organizational Learning


Exploitation

Refinement and extension of existing competencies, technologies, paradigms

Exploration

Experimentation with new alternatives

(March 1991)

Network is a resource for the firm because it is one of the sources through which organizational learning takes place. Organizational learning is one of the mechanisms through which firms increase knowledge, and thus innovate.

INTRODUCTION

COLLABORATION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE


How does the network facilitate exploration and exploitation?

Provides access to new knowledge elsewhere in the network (exploration)

Combined with the firm’s existing competencies provides both exploitation of its knowledge further, and also results in new knowledge creation.

Therefore, internal competencies and external links are complementary to each other.

But, how these processes take place also depend on the types of knowledge...

Networks and type of knowledge: tacit and explicit

Tacit knowledge is difficult to transfer, its basically knowledge about know-how, its sticky, embedded in people.

Explicit knowledge is codified, like in manuals, or books, it is easier to transfer among people.

INTRODUCTION

COLLABORATION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Some examples:

Collaborate to explore or exploit depends on:

1. life cycle of the firm (Oliver, 2001)

2. industry life cycle

(Rothermel and Deeds, 2004) 3. uncertainty and industry life cycle

(Nesta and Mangematin, 2002; Beckman et al., 2004).

Negative effect of leakages of valuable knowledge

Oxley and Sampson (2004) Mohr and Sengupta (2002) Dutta and Weiss (1997)

Organizational Learning


INTRODUCTION

COLLABORATION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

With Whom do Firms Collaborate?

In this strand of literature, bulk of papers focus on the physical characteristics of a network, and /or the physical position of the firm in the network.

Embeddedness

Granovetter’s idea of enbeddedness

Social networks of firms are decisive on their partner selection.

Firm level studies:

The more two firms know each other through others or directly, the more likely they will collab. Gulati (vairous years), Huggins (2000) , Baum et al (2005)

Social networks of managers have an effect.Donald and Westhpal (2003) Rosenkopf et al. (2001) Gulati and Westphal (1999)

Imitation and Isomorphism

DiMaggio and Powell, (83) idea of mimetic isomorphism

Similar firms or competitors imitate each other’s behaviour, and so that is how organizational forms spread.

Kenis and Knoke (2002) Garcia Pont and Nohria, 2002

Rosenkopf and Padula (2008) Investigating the Microstructure of Network Evolution: Alliance Formation in the Mobile Communications Industry OrgSci

INTRODUCTION


WHY NETWORKS?

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NETWORK STRUCTURE

Effect of Networks on Firm Performance

Effect of Embeddedness

Social Capital and Structural Holes

GoegraphicalDistricts

Trust Proximity and inno Third parties

Others

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WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

1. Effect of Embeddedness on Performance

A widely shared view: positive effect of embeddedness on performance

Echols and Tsai, 2005; Andersson et al. 2002; Uzzi and Gillespie, 2002

But there is also the concept of paradox of embeddness: (Uzzi 1997)

"the same processes by which embeddedness creates a requisite fit with the current environment can paradoxically reduce an organization ability to adapt” mainly by decreasing diversity, reduction of non redundant ties and sometimes causing overembeddedness.

Kim et.al (2006): “network inertia” which refers to the resistance on the side of firms to change their network portfolio.

Gilsing, Nooteboom, van Haverbeke, Duijsters, & Oord,(2008) Network embeddedness and the exploration of novel technologies: technological distance,betweenness centrality and density, Research Policy: effect of embeddedness on novelty and absorptive capacity. Robson, Katsikeas, Bello (2008)Drivers and Performance Outcomes of Trust in International Strategic Alliances: The Role of Organizational Complexity; Organization Science

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WHY NETWORKS?

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NETWORK STRUCTURE

1. Effect of Embeddedness on Performance

Result:

Ties composed of a mix of arms length and embedded ties are more conducive to increased performance in the apparel industry. But contasting result by Shipilov (2005) who finds that the combination of embedded and arms length ties is not beneficial in the Canadian investment banks.

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

2. Social capital, structural holes and performance of the firm

Burt (1992) argues that the competitive advantage of firms rests on their ability to fill structural holes between dense groups of firms. Low structural holes, and therefore high level of redundancy in ties make information exchange inefficient and thus not beneficial for competitive advantage.

Conversely, Coleman (1988) argues that taking place in a dense network with cohesive ties confers competitive advantage to the firm, because coordination is improved through repeated exchange with stable partners which facilitates the transfer of tacit knowledge. Social capital versus structural holes are analogous to the strong ties versus weak ties conceptualization of Granovetter (1973), respectively. Strong ties are usually associated with thick information exchange, efficient and effective transfer of tacit knowledge, and connote trust among partners. In this sense, strong ties are better to exploit existing knowledge, and to deepen the knowledge of the firm in specific areas

On the other hand, weak ties are associated with exploration, that is access to new areas of knowledge (Granovetter, 1973, Dyer and Nobeoka, 2000; Rowley et al., 2000).

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE


Ego is rich in social capital

Ego is filling a structural hole

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WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE


Ahuja (2000) how the position of the firm within the network influences its innovative output with a longitudinal study in the international chemicals industry. There are two kinds of benefits that a firm acquires through networks: firms access the resources (physical, skills, knowledge) of other firms, and second, networks enhance firms access to outside developments, like a major technological innovation, or failures through knowledge spillovers. structural holes have a negative impact on innovative output; Hite and Hesterly (2001) analyse how the changing resource needs of start-ups matches with the cohesive ties or structural holes in various stages of evolution of the firm. In particular, they state that as the firm passes from the emergent to a growing stage, the network shifts in response to changing resource needs. In initial phases, the network structure conducive to success is more cohesive and as the firm grows filling structural holes becomes more critical for success. McEvily and Zaheer (1999) find evidence in favor of bridging ties for competitive advantage, which are non redundant, infrequent and geographically dispersed.Gargiulo and Benassi (2000) stress the trade-off between the safety conferred by cohesive ties and the flexibility conferred by filling structural holes. Inkpen and Tsang (2005) how knowledge transfer can be facilitated in different types of networks with respect to different dimensions of social capital. Koka and Prescott (2002) different dimensions of social capital and demonstrate that each dimension has a different impact on firm performance (information dimensions, like volume, diversity and richness).

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE


The conflicting results of these studies which predict different best structures for different industries reveal that the effect of strong ties and weak ties on firm performance depends largely on the conditions surrounding the firm.

Rowley et al. (2000) investigate the conditions under which strong/ weak ties and close/ sparse networks are associated with firm performance. Their findings for semiconductor and steel industries reveal that weak ties are beneficial for exploration, especially in uncertain technological environments, and that strong ties are beneficial for exploitation, where uncertainty is low and competitive pressure is high.

Tiwana (2007) Do bridging ties complement strong ties? An empirical examination of alliance ambidexterity Strategic Management Journal

Giovanna Padula (2008) Enhancing the Innovation Performance of Firms by Balancing Cohesiveness and Bridging Ties

Dyer and Nobeoka's (2000) detailed study of the Toyota network also supports this argument.

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

3. Other Studies on Network Structure and Firm Performance A rich strand of literature examines the effect of partner portfolio on performance. Koka and Prescott (2008) Designing alliance networks: the influence of network position, environmental change, and strategy on firm performance Bae and Gargiulo (2004) When the focal firm has a large proportion of powerful partners, its control over resources can also be costly for the firm, which might outweigh the benefits of an alliance. Gulati and Higgins (2003) investigate the young biotechnology firms and their IPO performance and find that the benefits of links with venture capital and investment banks depend on the stage of market, while for strategic alliances there is no contingence. Beckman and Haunschild (2002) find that firm’s acquisition performance increases when its network partners are diverse in their experiences. Hoang and Rothaermel (2005) find that organizational learning through diverse range of partners results in higher performance then repeated alliance experience with a single partner in biotechnology.

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

3. Other Studies on Network Structure and Firm Performance A rich strand of literature examines the effect of partner portfolio on performance. Baum et al. (2000) analyse the performance of start-ups with respect to their alliance formation with others, having defined network efficiency by the diversity in the firms partners (universities, research institutes, rivals, etc.) Their results indicate that diversity in the partners competencies increases start-up performance in most aspects. Wuyts et.al (2002) report a similar finding where technological diversity among partners and repeated partnering increase ability to generate radical innovations. Phan and Peiridis (2000) underline the importance of conflict between partners in the successful knowledge transfer in an alliance. Goerzen and Beamish (2005) multinational performance is lower when the alliance network is composed of diverse partners.

Dovev Lavie, Stewart R. Miller (2008)Alliance Portfolio Internationalization and Firm Performance , Organization Science

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

3. Other Studies on Network Structure and Firm Performance

Transfer of CapabilitiesKnowledge transfer and international joint ventures: the case of NUMMI and General Motors Andrew C. Inkpen (2007) Strategic Management Journal

Dutta and Weiss (1997) investigate the relation between partnership type and innovativeness of the firm, and find that technologically innovative firms are usually involved in diverse range of partnership types so that transfer of tacit knowledge is restrained.

Mowery et al. (1998) investigate the transfer of firm capabilities by before and after alliance formation. Their findings indicate that the level of technological overlap increases after alliance formation, regardless of the initial motives underlying alliance (whether for market access or technological),

Mesquita, Anand, Brush (2008) resource based perspective n explaining performance of alliances

Anand and Khanna (2000) who look at the effect of learning to manage alliances,

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE


Transfer of CapabilitiesTsai (2001) who carries out an empirical study in petrochemicals and food manufacturing, and finds that it is the interaction between absorptive capability of the firm and network position that has a significant effect on innovative performance.

Kale et. Al (2000) investigate how the development of mutual trust facilitates transfer of know-how and also forms as a basis to overcome the effects opportunistic behaviour.

Larsson et.al (1998) analyse the learning effect of alliances in the context of learning strategies adopted by firms.

Dyer and Nobeoka (2000) study the Toyota network, and how its structure enhances the organizational learning of its members

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Transfer of Capabilities

Hagedoorn and Duuysters (2002) find that learning through exploratory networks is better for innovative performance than learning through exploitation networks.

Kuen-Hung Tsai (2009) Collaborative networks and product innovation performance: Toward a contingency perspective, Research Policy how absorptive capacity influences product innovation performance in different networks.

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3. Other Studies on Network Structure and Firm Performance Firm Size:

Singh and Mitchell (2005) find out that both entry and post entry collaboration contributes to superior performance, the extent of which is dependent upon who the collaborator is and upon the size of the firm in hospital software industry.

Rogers (2004) for the case of Australian firms find that effect of networks on innovation is significantly positive for small firms in manufacturing industry.

Bowles and Gintis (2004) look at the economic effects of ethnic and religious sharing of identity in business networks and find support for the idea that these networks can solve economic problems that are resistant to market or state based processes.

Park et. Al (2004) find a positive effect of alliances on firm value in market type alliances cpmpared to technology alliances.

Beverly Hirtle (2007) The impact of network size on bank branch performance Journal of Banking and Finance. (branch networks)

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NETWORK STRUCTURE

3. Other Studies on Network Structure and Firm Performance Firm Size:

Kim, J., Finkelstein, S. (2009).look at the effect of strategic and market Complementarity on Acquisition Performance in the U.S. Commercial Banking Industry, 1989-2001

Yadong Luo (2008) Structuring interorganizational cooperation: the role of economic integration in strategic alliances , strategic management journal: alliance performance.

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theoretical and simulation analysis of knowledge diffusion,

Cowan and Jonard (2001) demonstrate that small worlds outperform regular and random structures in the extent of knowledge diffusion. Cowan and Jonard (2004) find that small world produces highest knowledge growth, yet the most uneven distribution of knowledge among the actors. Cowan et. al (2003) explore the effects of interaction in networks compared to no-networks (no structure in communications). Their simulation results indicate that regular structures generate higher knowledge growth in industries with tacit knowledge and high technological opportunities; whereas when knowledge is codified and technological opportunities are lower, communication without any structure performs better. In a similar framework, Singh (2003) who investigates the relation between knowledge flow and the social distance between inventor teams, and finds that the existence of a social link between two teams is associated with larger knowledge flows.

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4. Industrial Districts

The literature on innovation in industrial districts has been growing impressively in the last two decades. This has partly been the result of a coinciding between two literatures; one is evolutionary and rejects a linear view of innovation (Nelson and Winter, 1982), and the other is literature on local and regional development economics.

In this strand of research:

learning has a localized nature (Maskell and Malmberg, 1999; Malmberg, 1997; Antonelli, 2000);

institutions have a central role in shaping regional innovation performance (Amin, 1999; Cooke and Morgan, 1998) and

innovation is viewed as a “dynamic collective learning” process which essentially takes place within local milieux (Camagni, 1991; Capello, 1999, Keeble et. Al, 1999). [1]

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Trust and social networks in industrial districts

One of the most important mechanisms underlying the innovation process in regions is trust among parties. It can be defined as a "cumulative product of repeated past interactions" (Ring and Van de Van, 1994).

According to a strand of research, because knowledge is tacit and only partly codifiable, face-to-face contacts is one of the most efficient ways to ensure its diffusion, which is accompanied by the development of trust among parties. These social networks have a positive effect on the extent of performance, and underlying them the most significant mechanism is trust (Jones et al., 1997; Lazerson, 1995).

The distinction between an industrial district and a network is underlined by Lechner and Dowling (1999), who stress that the existence of close ties among actors and social networks are the basis of regional networks. For the success of an industrial district not only the existence of a regional infrastructure, pooled skill, common legal and financial services are adequate; important part of success stories arises from cultural factors.

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4. Industrial DistrictsTrust and social networks in industrial districts

The increased collaboration between firms in Silicon Valley and the sharing of a common culture and social context in the region enabled to better foresee market conditions, shaping the rate and direction of technological developments to a large extent. On the other hand, firms in Route 128 were mostly self-sufficient vertical hierarchies, largely closed to external collaboration (Saxenian, 1994). Indeed, it has been shown in other studies that informal contacts have important influence on knowledge diffusion in regional clusters.

Dahl and Pederson (2004) confirm this for the case of regional cluster of wireless communication firms in Northern Denmark; and Owen-Smith and Powell (2004) in their study of the Boston metropolitan area find support that informal transmission mechanisms strengthens the information channels among physically proximate firms. Cooke and Wils (1999), for the case of Denmark, Ireland and Wales finds that social capital has a positive effect on innovation in clusters.Ruttena and Boekemab (2007) Regional social capital: Embeddedness, innovation networks and regional economic development TFSC

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Effect of proximity on innovation

Previous empirical research has confirmed that face-to-face contacts and geographical proximity are important factors facilitating the diffusion of innovations (Jaffe et al., 1993), fostering some forms of knowledge exchange (Morgan, 2004) and provide better access to information (Porter, 1990)

Bell (2005) finds for Canadian firms that locating in an industrial cluster and a central position of the manager network increases innovativeness. Aharanson et.al (2004) firnd that clustered firms are moreinnovative than geographically dispersed firms and clustering has a significant positive effect on innovation. Sonn and Storper (2003) also validate the positive effect of geographical proximity on innovation, looking at US patent citations. In another patent analysis, Almeida and Kogut (1999) find that regions are different from each other in terms of knowledge localization, and mobility of patent holders is the main mechanism that localizes knowledge in Silicon Valley. Kauffman et al. (2003) look at the effect of Internet on innovation networks and find out that Internet is complementary to local innovation networks.

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4. Industrial Districts Effect of proximity on innovationDespite the long established view that proximity increases innovativeness because of localized learning, a recent strand of literature cast doubt on this, questioning the postulate that mere geographical proximity is sufficient for learning and innovation.

Boschma ( 2004) stresses the importance of institutional and organizational proximity

Boschma and ter Wal (2005) show that it is not only local connections but also global connections

Rallet and Torre (1999) also support this finding for the case of three French regions.

Balconi et al., 2004; Sorenson, 2003; Agrawal et al., 2003 social connectedness might be more important than the mere geographical proximity

Bathelt et. Al (2004) emphasize the importance of both global and local interactions.

Doloreux (2004) local SMEs rely also on their linkages with customers and suppliers outside the region.

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4. Industrial Districts Effect of proximity on innovation

Oerlemans et. Al (2001) external linkages had stronger effect on innovative performance in the face of complexity.

Andersson and Karlsson (2004) underline the importance of accessibility rather than mere spatial proximity on innovation.

Lissoni (2001) knowledge can be highly codified, and even so it does not flow freely among all the firms in the cluster but rather within small communities.

Love and Roper (2001) for the case of UK, German and Irish firms also find no significant effect of external links and innovative performance of firms in geographical districts.

Bell and Zaheer (2007) Geography, Networks, and Knowledge Flow Geoffrey G. Bell, Akbar Zaheer, Organization Science

necessity to “move beyond” the geographical definition of “space” in economics (Amin and Cohendet, 2005)....

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4. Industrial Districts Networks involving third parties in industrial districts

Another strand of literature has approached networks from a wider context including not only firms but also research institutes, universities, and government bodies as well.

Hites and Hesterly (2001) study the relations of firms with regional institutes in metal working job shops,

Collinson and Gregson (2003) compare the three organizations established in USA, UK and Canada to promote new start-ups and the way they interact with networks of firms and other institutions in their respective regions.

Cooke (1996) underlines the role of not for profit organizations in setting up networks in districts,

Molina Morales et. Al (2002) state that one of the benefits of proximity is the possibility of access to external instiutions to get knowledge from outside.

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WHY NETWORKS?

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NETWORK STRUCTURE

Effect of external conditions on the emergence of an overall network structure

Small Worlds and Scale Free Networks

Network Evolution Models

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Network structure

During the recent years, there is a growing attention in the literature to understand and explain the overall structure of networks. Theoretical work is developing only slowly, due to the difficulties inherent in studying networks.

• most real world networks are characterized by high complexity: just as a perfectly regular network in which all nodes are tied to nearest n nodes may generate complexity as it evolves, the structure of the network itself may also be complex in a static framework (Strogatz, 2001).

• In most real world networks, there is high level of diversity in the type of ties in terms of their weight and content, and in the nodes themselves. (Strogatz, 2001).

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Network structure

• Under these circumstances building theories to explain network structure is difficult since simplifications in their structure and focusing on relevant aspects (as determined by the context) of the network is inevitable, rather than covering all aspects within a unique theoretical framework.

• Despite all the difficulties, studying networks and how they evolve is valuable in many aspects, because a dynamic network perspective enables to see how economic outcomes are influenced by nature of networks and how networks influence economic processes (Kirman, 1997).

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Network structure

Network structure: The role of external conditionsemergence of network structure as a function of external conditions, which refer to the knowledge base, technological conditions in the industry, as well as uncertainty and industry events.

Madhavan et al. (1998) study how specific industry events shape the structure of the network in the steel industry. They measure structure by centrality and interblock relations, and focus on industry specific events like a major technological change, entry of a powerful competitor, a change in the regulatory infrastructure, and dramatic shifts in consumer preferences.

Koka et al (2006) investigate how external effects and firm strategy shape the evolution of networks.

Orsenigo et al. (1998, 2000) who construct a relation between the dynamics of the knowledge base in biotechnology, and the evolution of the industry, and detect a close relation between the two.

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Network structure

Network structure: The role of external conditions

Kash and Rycroft (2000) and Frenken (2000) who model the relation between complexity of knowledge and networks. In a simulation model,

Cowan et. al. (2004, 2006) model an evolving network where knowledge carrying agents match with each other to combine their knowledge levels (and thus innovate). They investigate the resulting network structures as a function of type of innovation. They distinguish between two innovation regimes. In one of them diversity is important in innovation, in the other knowledge level is important. They show that different network structures emerge depending on innovation regime, and small worlds are a possibility in a certain parameter space.

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Network structure

Network structure: The role of external conditionsMcKelvey et al. (2003) investigate the biotechnology as a sectoral system of innovation, and explore in detail the historical evolution of the network of relations, as well as how these were shaped by the evolution of the knowledge base, regulatory environment and the market. Ozman (2005) studies the breadth and depth of knowledge bases and how they result in different organizational structures as expressed as a network.(Pyka, 2000; Rosenkopf and Tushman, 1998; Nesta and Mangematin (2002). aim to detect the relation between uncertainty or stage in the industry life cycle and networks Rosenkopf and Tushman (1998) examine the co-evolution of community networks and technology cycles in the flight simulation industry. They distinguish between eras of ferment, where technical uncertainty is high, and periods after which a dominant design emerges, which are the periods of incremental change. Their findings indicate that the intensity of the relations among actors increases during the former period, and they stress the mutual relation between technology and networks.

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Network structure


mutual relation between the pattern of technological development and networks firms.

Soh and Roberts, 2000 networks also influence the way technology evolves Barley (1990) who shows how the characteristics of technology also change and modify social networks in an organization. Burkhardt and Brass (1990) on how technology effects network structure.

Empirical research in network evolution (limited because of data)

Powell et al. (1996) ,Walker et al. (1997) who find evidence of a path dependent learning process specific to biotechnology industry that shapes the network of relations among firms, Garcia Pont and Nohria (2002) adopt a dynamic perspective where networks form through mimetism in the automobile industry Gulati (1999) who studies partner selection in relation to social networks.

INTRODUCTION


WHY NETWORKS?

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Singh (2005), and Fleming et al. (2006).

Network structure


(Balconi et al. 2004).interpersonal communication among scientists, engineers, or technologists play a vital role in the way knowledge diffuses. In this sense, individual inventors are the carriers of knowledge in their respective social networks. But detecting social networks on a large scale is not easy. One promising avenue in the detection of these networks is by using patent data, because it provides information on co-invention and citation patterns among patents

INTRODUCTION


WHY NETWORKS?

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NETWORK STRUCTURE

Network structure

Small World and Scale free networks

Watts and Strogatz (1998) have been the first to systematically explore in mathematical terms what was already known to many people: that the world is small. Since then, a vast amount of research has been conducted on the so called small world networks.

Small world networks are characterised by two parameters, clustering and path length. Watts and Strogatz (1998) demonstrate that the small world region lies somewhere in between a perfectly cliquish regular network with high clustering and high path lengths between nodes, and random networks with no clustering and short average path lengths. Small worlds, which lie in between these two extremes are characterised by high clustering (local connections, where nodes have ties with nearest nodes), but at the same time, via a few short cuts between distant agents, low average path lengths.

INTRODUCTION


WHY NETWORKS?

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NETWORK STRUCTURE

Network structure


Source: Cowan and Jonard (2003)

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WHY NETWORKS?

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Network structure


As empirical work in many areas like the World Wide Web, scientific collaboration, movie actor networks reveal, most of the real world networks exhibit small world propertiesCowan and Jonard, 2001, 2003, 2004; Cowan et al, 2001 relation with knowledge diffusion Robin Cowan and Nicolas Jonard (2008) If the alliance fits …: Innovation and network dynamics in relation with network formation (see section 1)

Baum et al (2003) investigate the emergence of small world networks among Canadian investment banks, and Carayol and Roux (2004) examine the possibility of the emergence of stochastically stable small worlds in the face of small perturbations in the rules of link formation.Bekkers et al. (2002) find evidence of small world properties in technological alliances.Barabasi and Albert (2000) provide a survey on complex networks, which incorporates the state of the art developments in the theoretical literature. The survey includes a detailed account of empirical work in topology of various real world networks, as well as recent theories on random networks, small worlds, percolation theory and scale free networks.

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Network structure


As Barabasi (2002) discusses, small world explanations have limitations in modelling real world networks in some important aspects. In these models the network size is held fixed, whereas in the real world networks grow, mostly following certain rules like preferential attachment.

Basically preferential attachment means popular agents attract more agents. Indeed, they have shown with the World Wide Web that, the degree distribution follows a power law, which means that there are a few nodes attracting majority of the nodes, and majority of the nodes have few connections. Underlying scale free networks are basically these two rules, preferential attachment, and a growing network.

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

Network structure


Following their paper, the network literature has been witnessing an increasing interest in scale free networks (Solla de Price (1967) for the initial foundations of scale free networks).

Riccaboni and Pammolli (2002), where they compare biotechnology and ICT industries. Their results reveal that in both ICT and LS (life sciences) the frequency distribution of firm links follow a power law, and thus they are characterised by a scale free structure.

INTRODUCTION


WHY NETWORKS?

FIRM PERFORMANCE

NETWORK STRUCTURE

INTRODUCTION


WHY NETWORKS?

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NETWORK STRUCTURE

Network structure

Game theoretical and evolutionary models

Most of these models explore the dynamics of networks, where self interested actors interact and result in the emergence of an overall network structure, under different mechanisms for interaction and different behavioural rules.

These models analyse how the network forms and evolves, the feedback mechanism conferred by the network, and the relationship between stability and efficiency of the network.

This literature has been developing rapidly in the last decade and a nice survey can be found in Jackson (2003) and Cowan (2004) on knowledge diffusion.

INTRODUCTION


WHY NETWORKS?

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NETWORK STRUCTURE

Network structure

The term network dynamics connotes the change of the network through time. As Doreian and Stokman (1997) point out, however,

network dynamics and network evolution have different meanings. Network evolution refers to understanding the dynamics of the network via some understood process, so evolution of the network connotes understanding the rules governing the sequence of changes through time (p.5).

INTRODUCTION


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Network structure In network evolution models, the rules governing addition and deletion of ties for each node are imposed in the micro level.These rules are usually based on the trade-off between the costs and benefits of forming a link. Therefore, the analysis is not being made from the planners' perspective, but from the perspective of individual incentives (Bala and Goyal, 2000). Jackson and Wolinsky (1996): They investigate the stability and efficiency of networks, where links are non-directed, and there is a cost and benefit of forming links. They show that achieving stability and efficiency at the same time depends on the value allocation rules among network members, and need not always intersect. Jackson, 2001, efficiency in different waysWatts, 2001 incorporating both direct and indirect linksJackson and Watts, 2001 introducing small perturbations Watts, 2002 introducing non-myopic agentsBala and Goyal, 2000; Goyal and Vega Redondo, 2000; Skymrs and Pemantle, 2000 learning and for a nice survey, Goyal , 2003; Goyal and Joshi, 2003; Goyal and Moraga, 2001; Kranton and Minehart, 2000; Zirulia, 2004 introducing different market contextsHea and Fallah (2008) Is inventor network structure a predictor of cluster evolution? Empirical, telecoms, TFSC

INTRODUCTION


WHY NETWORKS?

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Network structure Carayol and Roux, 2004 stochastic stability Carayol et al, 2005 use of genetic algorithms, Cowan et al 2006 and knowledge diffusion and network evolution Cantonoa and Silverberg (2009) A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies, Technological Forecasting and Social Change

Conclusion

Networks are very important in innovation

Studies involve many different sectors, methods, approaches... Makes it difficlt to arrive at robust conclusions.

However, there is some consensus on certain questions

More research is needed...

DIMETIC MARCH 2009 Muge ozman Institut telecom telecom & management sudparis Inter-fırm networks...

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