It is well known from previous research activities that R&D collaboration among economic actors for knowledge production is
very important. An accompanying analysis of the impact of R&D collaboration on innovative performance has to be conducted
for transferring knowledge to the globalized knowledge-based economy. When we first investigated previous research concerning
R&D collaboration, we found some limitations in the analysis methodology. In order to overcome these limitations in previous
research, we applied a Bayesian network for analyzing the impact of R&D collaboration in Korean firms on their innovative
Intermediaries in a technological knowledge network have recently been highlighted as crucial innovation drivers that accelerate
technological knowledge flows. Although the patent network analysis has been frequently used to monitor technological knowledge
structures, it has examined only sources or recipients of the technological knowledge by mainly estimating technological knowledge
inflows or outflows of a network node. This study, therefore, aims to identify technological knowledge intermediaries when
a technology-level knowledge network is composed of several industries. First, types of technological knowledge flows are
deductively classified into four types by highlighting industry affiliations of source technologies and recipient technologies.
Second, a directed technological knowledge network is generated at the technology class level, using patent co-classification
analysis. Third, for each class, mediating scores are measured according to the four types. The empirical analysis illustrates
the Korea’s technological knowledge network between 2000 and 2008. As a result, the four types of mediating scores are compared
between industries, and industry-wise technological knowledge intermediaries are identified. The proposed approach is practical
to explore converging processes in technology development where technology classes act as technological knowledge intermediaries
among diverse industries.
This paper suggests an
international benchmarking method of disembodied knowledge flow structure.
Using patent citation as a proxy measure of disembodied knowledge flow,
national knowledge network is developed. Structural equivalence measure is
applied to comparing the knowledge network of Korea and Taiwan with that of
USA. Static and dynamic comparison make it possible to benchmark disembodied
knowledge flow structure efficiently and identify convergent and divergent industries between
developing countries and USA. It is also a meso-study that could be conducive
to building a comprehensive analytical framework of national innovation system.
The notion of knowledge-based economy premises that technological knowledge be created, accumulated and disseminated through
the interactive learning among principal actors in the national system. This paper analyzes, from a dynamic perspective, the
structure of inter-industrial technological knowledge. Both human-driven disembodied channel and capital-driven embodied channel
are investigated based on network analysis. The set of empirical data covers the Korean manufacturing sector during the 1980s.
Overall, density of network tends to be increasing over time, implying that knowledge network becomes expanded and intensified.
A number of distinctive features are identified between knowledge types and industrial categories. The findings in turn render
important policy implications that should be addressed when developing technology policy. Clearly, the policy framework needs
to be industry-specific and country-specific in accordance with the development stage and industrial structure of reference