Summary Articles Spec. Course S&I
Geography of innovation – Stefano Breschi
Firms’ innovative and economic performance depend not only on their own
investment in R&D, but also are affected by the R&D spending of other firms and
universities. The spatial clustering of innovation should be explicitly modeled as
emerging interaction between learning, competition and selection processes,
which take place among firms, and geographical areas.
Spatial dimension in technical change, dimensions of technological regimes:
Opportunity conditions: probability of innovating for any given amount of
resources invested in search. The availability of human capital, location of
universities and firms’ HQ the more dense network of interaction exists.
Appropriability conditions: summarize the possibilities of protecting
innovations from imitation. High degrees of technological appropriability
are likely to lead to high levels of geographical concentration of
innovative activities.
Cumulativeness of technological knowledge: degree of persistence or
serial correlation among subsequent innovations.
Nature of knowledge base: properties of the knowledge upon which firms’
innovative activities are based (tacit vs. codified, complex vs. simple). The
more the knowledge base is tacit, the more geographically concentrated
the population of innovators will be.
Results:
Spatial patterns of innovation differ systematically across technological
classes. Industry- and technology-specific factors play a fundamental role
in shaping the spatial patterns of innovation across countries.
There are systematic relationships between spatial and sectoral parts of
innovation. Three distinctive groups:
o Traditional classes and widening sectoral pattern (low
concentration and high turbulence of innovators with low spatial
concentration and low cumulativeness of innovative activities
o Mechanical engineering and widening with low spatial
concentration and high spatial cumulativeness of innovative
activities
, o Chemical and electronic with deepening sectoral pattern (high
concentration and low turbulence of innovators with high levels of
spatial concentration and cumulativeness
The combination of widening-spatially concentrated and deepening-
spatially diffused patterns of innovation characterizes only specific
technological classes in some countries as a result of the industrial history
of the national system.
Spatial cumulativeness is a fundamental variable in explaining spatial
concentration of innovative activities and the regions’ technological
performance.
Complementarities of internal R*D and alliances with different partner types
– Noseleit & Faria
The effectiveness of firms’ internal R&D efforts to produce innovation output is
dependent on the type and intensity of collaboration with external partners.
More distance in the knowledge base across partnering firms increases the
chances to use internal sources more efficiently. Similarities in knowledge base
facilitate the knowledge transfer.
External knowledge comes from universities, leading clients and suppliers or
even competitors. Strategic alliances allow firms to access external knowledge.
Results:
Collaborations with external partners can boost but also restrict firms’
abilities to transform internal R&D efforts into successful innovations.
Internal R&D efforts become more productive when participating in
technology alliances in which partners within the same industry
dominate. The effect is stronger when collaborating with firms in related
industries.
The strength of strong ties in the creation of innovation – Rost
Professional networks are central to the knowledge creation process.
Coleman’s view: Social capital is an internal or bonding view, closure of the
network is an important prerequisite for social capital. Relationship quality
between exchange partners is important to explain when social capital
investments will capitalize. Each actor has control over certain resources and
interests in certain resources and events.
Burt’s view: social capital as external ties focused on competitive goals.
Structural configuration of networks plays a major role.
Both views may complement each other, both relational and structural aspects
are crucial for the generation of new knowledge.
Results:
Strong ties to exchange partners are prerequisite for innovation
Strong ties become most beneficial when combined with weak network
architectures, including some structural holes offer information and
Geography of innovation – Stefano Breschi
Firms’ innovative and economic performance depend not only on their own
investment in R&D, but also are affected by the R&D spending of other firms and
universities. The spatial clustering of innovation should be explicitly modeled as
emerging interaction between learning, competition and selection processes,
which take place among firms, and geographical areas.
Spatial dimension in technical change, dimensions of technological regimes:
Opportunity conditions: probability of innovating for any given amount of
resources invested in search. The availability of human capital, location of
universities and firms’ HQ the more dense network of interaction exists.
Appropriability conditions: summarize the possibilities of protecting
innovations from imitation. High degrees of technological appropriability
are likely to lead to high levels of geographical concentration of
innovative activities.
Cumulativeness of technological knowledge: degree of persistence or
serial correlation among subsequent innovations.
Nature of knowledge base: properties of the knowledge upon which firms’
innovative activities are based (tacit vs. codified, complex vs. simple). The
more the knowledge base is tacit, the more geographically concentrated
the population of innovators will be.
Results:
Spatial patterns of innovation differ systematically across technological
classes. Industry- and technology-specific factors play a fundamental role
in shaping the spatial patterns of innovation across countries.
There are systematic relationships between spatial and sectoral parts of
innovation. Three distinctive groups:
o Traditional classes and widening sectoral pattern (low
concentration and high turbulence of innovators with low spatial
concentration and low cumulativeness of innovative activities
o Mechanical engineering and widening with low spatial
concentration and high spatial cumulativeness of innovative
activities
, o Chemical and electronic with deepening sectoral pattern (high
concentration and low turbulence of innovators with high levels of
spatial concentration and cumulativeness
The combination of widening-spatially concentrated and deepening-
spatially diffused patterns of innovation characterizes only specific
technological classes in some countries as a result of the industrial history
of the national system.
Spatial cumulativeness is a fundamental variable in explaining spatial
concentration of innovative activities and the regions’ technological
performance.
Complementarities of internal R*D and alliances with different partner types
– Noseleit & Faria
The effectiveness of firms’ internal R&D efforts to produce innovation output is
dependent on the type and intensity of collaboration with external partners.
More distance in the knowledge base across partnering firms increases the
chances to use internal sources more efficiently. Similarities in knowledge base
facilitate the knowledge transfer.
External knowledge comes from universities, leading clients and suppliers or
even competitors. Strategic alliances allow firms to access external knowledge.
Results:
Collaborations with external partners can boost but also restrict firms’
abilities to transform internal R&D efforts into successful innovations.
Internal R&D efforts become more productive when participating in
technology alliances in which partners within the same industry
dominate. The effect is stronger when collaborating with firms in related
industries.
The strength of strong ties in the creation of innovation – Rost
Professional networks are central to the knowledge creation process.
Coleman’s view: Social capital is an internal or bonding view, closure of the
network is an important prerequisite for social capital. Relationship quality
between exchange partners is important to explain when social capital
investments will capitalize. Each actor has control over certain resources and
interests in certain resources and events.
Burt’s view: social capital as external ties focused on competitive goals.
Structural configuration of networks plays a major role.
Both views may complement each other, both relational and structural aspects
are crucial for the generation of new knowledge.
Results:
Strong ties to exchange partners are prerequisite for innovation
Strong ties become most beneficial when combined with weak network
architectures, including some structural holes offer information and
