Inter-fırm networks and
ınnovatıon
DIMETIC MARCH 2009
Muge ozman
Institut telecom
telecom & management sudparis
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Networks are considered to be important mechanisms that change economic
and social outcomes. Tremendous increase in theoretical and empirical
research on networks in the economics and management literature.
One of the fields in which network research has grown at an impressive rate is
innovation and technological change, as it is now accepted that innovation
is most effectively undertaken as a collective process in which networks play a
central role.
Innovation is a complex process, which includes many actors and their
interactions like universities, government agencies, public and private labs...
These interactions take place through networks. Firms are the main actors in
the process of innovation and their interactions with each other shape
innovative activities through knowledge diffusion and learning.
Research is highly interdisciplinary: physics, management, economics of
technological change, geography, sociology...
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In this lecture, we make an overview of research on firm networks and what
they imply for innovation and technological change, the theoretical and
empirical results obtained up to now in the field. What are the possible future
directions 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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3000
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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.
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Source: F. Deroian, Z. M’Chirgui and C. Milelli, Evidences on inter-Firm R&D partnerships in three
high-tech industries, GREQAM WorkinG Paper, 2007
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Source: F. Deroian, Z. M’Chirgui and C. Milelli, Evidences on inter-Firm R&D partnerships in three
high-tech industries, GREQAM WorkinG Paper, 2007
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Source: F. Deroian, Z. M’Chirgui and C. Milelli, Evidences on inter-Firm R&D partnerships in three
high-tech industries, GREQAM WorkinG Paper, 2007
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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".
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Network structure
Architecture of networks, stability,
efficiency
Effect of network
structure on
performance
External
conditions
Firm performance
Capabilities, innovation,
etc.
Proximity
Knowledge base
Complexity
uncertainty
Effect of firm
characteristics
on formation
Origins of Networks
Why firms collaborate?
Which firms collaborate?
With whom do they collaborate?
Models of
network
evolution
Effect of
network
structure on
formation
Network Structure
Network Structure
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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
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Why do firms collaborate? Which firms Collaborate?
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) efficiency
increasing the internal input-output ratio of the organization,
e) stability
as an adaptive response to environmental uncertainties,
f) legitimacy
to improve reputation, image, prestige.
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Why do firms collaborate? Which firms Collaborate?
Interdependence in Resources
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 selfsufficient, 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.
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Why do firms collaborate? Which firms Collaborate?
Interdependence in Resources
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
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Why do firms collaborate? Which firms Collaborate?
Interdependence in Resources
Source: Hagedoorn (2002)
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Why do firms collaborate? Which firms Collaborate?
Organizational Learning
Exploration
Experimentation with
new alternatives
Exploitation
Refinement and extension of
existing competencies,
technologies, paradigms
(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.
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Why do firms collaborate? Which firms Collaborate?
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 knowhow, its sticky, embedded in people.
Explicit knowledge is codified, like in manuals, or books, it is easier to
transfer among people.
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Why do firms collaborate? Which firms Collaborate?
Organizational Learning
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)
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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
Imitation and Isomorphism
Granovetter’s idea of enbeddedness
DiMaggio and Powell, (83) idea of
mimetic isomorphism
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)
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
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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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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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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.
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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).
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2. Social capital, structural holes and performance of the
firm
Ego is rich in social capital
Ego is filling a structural hole
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2. Social capital, structural holes and performance of the firm
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).
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2. Social capital, structural holes and performance of the
firm
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.
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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.
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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
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3. Other Studies on Network Structure and Firm Performance
Transfer of Capabilities
Knowledge 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,
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3. Other Studies on Network Structure and Firm Performance
Transfer of Capabilities
Tsai (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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3. Other Studies on Network Structure and Firm Performance
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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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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3. Other Studies on Network Structure and Firm Performance
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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4. Industrial Districts
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 Districts
Trust 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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4. Industrial Districts
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 innovation
Despite 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.
WHY
NETWORKS?
INTRODUCTION
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 conditions
emergence 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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INTRODUCTION
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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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INTRODUCTION
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Network structure: The role of external conditions
McKelvey 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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INTRODUCTION
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Network structure
Network structure: The role of external conditions
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.
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INTRODUCTION
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Network structure
Network structure: The role of external conditions
(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
Singh (2005), and Fleming et al. (2006).
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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.
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Network structure
Small World and Scale free networks
Source: Cowan and Jonard (2003)
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Network structure
Small World and Scale free networks
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
properties
Cowan 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.
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Small World and Scale free networks
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.
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Network structure
Small World and Scale free networks
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.
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INTRODUCTION
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INTRODUCTION
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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.
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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).
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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 ways
Watts, 2001 incorporating both direct and indirect links
Jackson and Watts, 2001 introducing small perturbations
Watts, 2002 introducing non-myopic agents
Bala 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 contexts
Hea and Fallah (2008) Is inventor network structure a predictor of cluster
evolution? Empirical, telecoms, TFSC
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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...