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Knowledge context, learning and
innovation: an integrating
framework
Stephen Roper1, James H Love2 and Ying Zhou2
June 2014
Objectives of our paper
• In econometric studies of innovation ‘context’
is often reduced to industry and geographic
dummies
• Increasingly allow for elements of ‘openness’
through linkages, however
• But is this the whole story? Here want to
suggest a broader integrating framework
linking
– Firms’ knowledge context
– Firms’ innovation aspirations and capabilities
– Mechanisms for gathering knowledge
– Innovation outputs
• Our argument has three elements:
– Profiling knowledge context – spatial, network
and industry elements
– How do firms access knowledge for
innovation? Interactive, non-interactive and
spillover mechanisms
– Potential mediating factors which may
influence the knowledge context – innovation
relationship : strategy and ‘encoding capacity’
Understanding the knowledge
context
Dimensions of knowledge
context
• Perhaps (?) three main aspects of knowledge context –
spatial, network and industrial
• Spatial – perhaps key here is represented by quote:
– ‘local knowledge is thus conceptualized as a semi-public good
that is spatially bounded, and access to which requires nothing
more than cluster membership.
– Next, local knowledge exchange is prompt or spontaneous
because local firms are assumed to be more willing to share
knowledge and exchange ideas with other local actors as a
result of shared norms, values, and other formal and informal
institutions that hold down misunderstanding and opportunism’
(He and Wong, 2012, p. 542).
Dimensions of knowledge
context
• Networks – network structure and density both linked to innovation
outcomes both positively and negatively through lock-in.
• But also note:
– ‘The mere presence, or absence, of key institutional elements of the
local or regional innovation system also affects their innovative
capacity and their potential to serve as nodes for cluster development.
Many clusters enjoy the knowledge assets and research infrastructure
that are necessary for the development of an innovation-based
development strategy, but they differ dramatically in their capacity to
mobilize these assets in the pursuit of such a strategy’ (Wolfe 2009, p.
186).
• Industry – technological opportunities and appropriability
conditions vary across industries so also important
Knowledge context – towards an
integrated view
Types of effect (key words):
1 – regional structure and
characteristics, regional institutions
2 – business groups or networks, social
networks, strategic partnerships
3 – industry structure, sectoral
characteristics
4 – local networks, regional networks,
community networks
5 – trade associations, supply chain
linkages, supply networks
6 – industry districts, agglomeration,
regional specialisations
7 – regional cluster networks,
openness in a cluster
Accessing external knowledge
Accessing external knowledge – 3
main mechanisms
• Interactive learning – purposive relationships or
partnerships which involve mutual learning (perhaps
more exploratory). Leads to notions of breadth etc.
• Non-interactive learning – purposive knowledge
seeking but learning is one sided (copying, imitation
etc.). Also in CIS.
• Spillovers – ‘being there’- here we mean ‘un-priced,
and unintentional, knowledge externalities which
result from the characteristics of knowledge as a semi-
public or public good’ (Sadri, 2011).
From knowledge to innovation
Strategic and organisational
moderators
• Firms within a given industry/spatial/network context innovate
differently.
• What explains this heterogeneity? And, inter alia shapes firms’ links
to the knowledge climate
• Two main factors (?)
– Innovation strategy – ambition, risk appetite, innovation v imitation -
> knowledge seeking
– Encoding capacity – the assimilation element of ACAP (we dealt with
the search element already)
• ‘encoding capacity reflects firms’ ability to make use effectively of incoming
knowledge for innovation, and that encoding capacity will therefore play a
moderating role in the relationship between any given level of external
knowledge and marketable innovation’.
An integrating framework
Integrating framework
Current work …

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Knowledge context, learning & Innovation. Druid 2014

  • 1. Knowledge context, learning and innovation: an integrating framework Stephen Roper1, James H Love2 and Ying Zhou2 June 2014
  • 2. Objectives of our paper • In econometric studies of innovation ‘context’ is often reduced to industry and geographic dummies • Increasingly allow for elements of ‘openness’ through linkages, however • But is this the whole story? Here want to suggest a broader integrating framework linking – Firms’ knowledge context – Firms’ innovation aspirations and capabilities – Mechanisms for gathering knowledge – Innovation outputs • Our argument has three elements: – Profiling knowledge context – spatial, network and industry elements – How do firms access knowledge for innovation? Interactive, non-interactive and spillover mechanisms – Potential mediating factors which may influence the knowledge context – innovation relationship : strategy and ‘encoding capacity’
  • 4. Dimensions of knowledge context • Perhaps (?) three main aspects of knowledge context – spatial, network and industrial • Spatial – perhaps key here is represented by quote: – ‘local knowledge is thus conceptualized as a semi-public good that is spatially bounded, and access to which requires nothing more than cluster membership. – Next, local knowledge exchange is prompt or spontaneous because local firms are assumed to be more willing to share knowledge and exchange ideas with other local actors as a result of shared norms, values, and other formal and informal institutions that hold down misunderstanding and opportunism’ (He and Wong, 2012, p. 542).
  • 5. Dimensions of knowledge context • Networks – network structure and density both linked to innovation outcomes both positively and negatively through lock-in. • But also note: – ‘The mere presence, or absence, of key institutional elements of the local or regional innovation system also affects their innovative capacity and their potential to serve as nodes for cluster development. Many clusters enjoy the knowledge assets and research infrastructure that are necessary for the development of an innovation-based development strategy, but they differ dramatically in their capacity to mobilize these assets in the pursuit of such a strategy’ (Wolfe 2009, p. 186). • Industry – technological opportunities and appropriability conditions vary across industries so also important
  • 6. Knowledge context – towards an integrated view Types of effect (key words): 1 – regional structure and characteristics, regional institutions 2 – business groups or networks, social networks, strategic partnerships 3 – industry structure, sectoral characteristics 4 – local networks, regional networks, community networks 5 – trade associations, supply chain linkages, supply networks 6 – industry districts, agglomeration, regional specialisations 7 – regional cluster networks, openness in a cluster
  • 8. Accessing external knowledge – 3 main mechanisms • Interactive learning – purposive relationships or partnerships which involve mutual learning (perhaps more exploratory). Leads to notions of breadth etc. • Non-interactive learning – purposive knowledge seeking but learning is one sided (copying, imitation etc.). Also in CIS. • Spillovers – ‘being there’- here we mean ‘un-priced, and unintentional, knowledge externalities which result from the characteristics of knowledge as a semi- public or public good’ (Sadri, 2011).
  • 9. From knowledge to innovation
  • 10. Strategic and organisational moderators • Firms within a given industry/spatial/network context innovate differently. • What explains this heterogeneity? And, inter alia shapes firms’ links to the knowledge climate • Two main factors (?) – Innovation strategy – ambition, risk appetite, innovation v imitation - > knowledge seeking – Encoding capacity – the assimilation element of ACAP (we dealt with the search element already) • ‘encoding capacity reflects firms’ ability to make use effectively of incoming knowledge for innovation, and that encoding capacity will therefore play a moderating role in the relationship between any given level of external knowledge and marketable innovation’.