Proceedings of the 12th European Conference on Knowledge ...

Marta Christina Suciu et al
Luintel, K. B. and M. Khan (2005) examine <strong>the</strong> dynamics <strong>of</strong> knowledge production for a panel <strong>of</strong> 19
OECD countries. A new and unique data set is used to proxy <strong>the</strong> domestic flows <strong>of</strong> “new-to-<strong>the</strong>-world”
knowledge and ideas. The cross-country heterogeneity in <strong>the</strong> production <strong>of</strong> knowledge and <strong>the</strong>
endogenous nature <strong>of</strong> <strong>the</strong> process are addressed in <strong>the</strong>ir paper. The parameters <strong>of</strong> <strong>the</strong> knowledge
production function point to large cross-country differences. Domestic and foreign stocks <strong>of</strong>
knowledge and ideas have a net positive effect on <strong>the</strong> production (flows) <strong>of</strong> new ideas. Countries with
a low domestic knowledge base appear to improve <strong>the</strong>ir total factor productivity considerably through
<strong>the</strong> accumulation <strong>of</strong> knowledge. This effect is very modest for countries that already have a sizeable
domestic knowledge base. Given <strong>the</strong> heterogeneous nature <strong>of</strong> knowledge production across OECD
countries, R&D policy will need to be adapted to <strong>the</strong> specific nature <strong>of</strong> each country as <strong>the</strong> one-sizefits-all
approach may not be effective.
An OECD (2009) study modeling a knowledge production function and using data from OECD
countries showed that investments in R&D have a positive impact on patent activity in all categories
considered (R&D expenditures by businesses, public sector, higher education institutions (HEI),
private non-pr<strong>of</strong>it sector). A surprising result may be <strong>the</strong> fact that R&D expenditures in HEI influences
patenting activities in a negative way and so does <strong>the</strong> effect <strong>of</strong> personnel in higher education –
however, this may reflect <strong>the</strong> lack <strong>of</strong> commercialization in HEI.
Gurmu and Shiferaw (2010) estimate a knowledge production function for university patenting using
an individual effects negative binomial model. They control for Research and Development
expenditures, research field, and <strong>the</strong> presence <strong>of</strong> a Technology Transfer Office. We distinguish
between three kinds <strong>of</strong> researchers: faculty, postdoctoral scholars (postdocs), and PhD students. For
<strong>the</strong> latter two, we also distinguish by visa status. We find patent counts to relate positively and
significantly to <strong>the</strong> number <strong>of</strong> PhD students and number <strong>of</strong> postdocs. Our results also suggest that not
all graduate students and postdocs contribute equally to patenting but that contribution is mediated by
citizenship and visa status
Ahrweiler, P (2011) applies <strong>the</strong> agent‐based SKIN model (Simulating Knowledge Dynamics in
Innovation Networks) to university‐industry links. The model builds on empirical research about
innovation networks in knowledge‐intensive industries with procedures relying on <strong>the</strong>oretical
frameworks <strong>of</strong> innovation economics and economic sociology. The experiments compared innovation
networks with and without university agents. Results showed that having universities in <strong>the</strong>
co‐operating population <strong>of</strong> actors raised <strong>the</strong> competence level <strong>of</strong> <strong>the</strong> whole population, increased <strong>the</strong>
variety <strong>of</strong> knowledge among <strong>the</strong> firms, and increased innovation diffusion in terms <strong>of</strong> quantity and
speed. Fur<strong>the</strong>rmore, firms interacting with universities are more attractive for o<strong>the</strong>r firms when new
partnerships are considered. These results can be validated against empirical findings. The simulation
confirms that university‐industry links improve <strong>the</strong> conditions for innovation diffusion and enhance
collaborative arrangements in innovation networks.
4. The role <strong>of</strong> higher education institutions
In a knowledge-based economy characterized by <strong>the</strong> production, transmission and dissemination <strong>of</strong>
knowledge, <strong>the</strong> universities play a unique role in all <strong>the</strong>se processes. An efficient knowledge
management in a university will ensure <strong>the</strong> best use <strong>of</strong> available resources and best use <strong>of</strong> <strong>the</strong>
knowledge as a key resource; creation / generation <strong>of</strong> new knowledge; understanding and
awareness; capturing, organizing, storing <strong>the</strong> knowledge at individual, group and organization level;
and knowledge transfer to o<strong>the</strong>r persons or o<strong>the</strong>r organizations. Knowledge management has <strong>the</strong>
opportunity to promote and establish a culture <strong>of</strong> innovation and learning within <strong>the</strong> institution. A
learning environment is very important for <strong>the</strong> new learning which takes place within <strong>the</strong> institution.
Universities have traditionally been involved in <strong>the</strong> development <strong>of</strong> new concepts through research.
Research performed by universities has been a contributor towards innovative ideas and products.
Through technology transfer, <strong>the</strong>se new discoveries can be transferred to practicing businesses or
o<strong>the</strong>r sectors. By remaining in one’s field, an innovator can take <strong>the</strong> research from <strong>the</strong> universities and
develop new concepts or products that may be brought to market. The first type <strong>of</strong> innovation is not
restricted to formal research as typically presented by a university. Many private laboratories and
research groups contribute to new findings also. Once a new discovery has been made it must be
made practical. This is <strong>the</strong> second phase <strong>of</strong> innovation. The applications <strong>of</strong> new concepts, <strong>the</strong> second
part <strong>of</strong> innovation is typically in <strong>the</strong> hands <strong>of</strong> practitioner innovators. By staying in business
management and technology advances, for example, individuals and groups have developed new
online services based on <strong>the</strong> researchers developing <strong>the</strong> concept <strong>of</strong> <strong>the</strong> Internet.
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