Determining factors of interregional research collaboration in Germany's biotech network: Capacity, proximity, policy?

Mitze, Timo; Strotebeck, Falk

doi:10.1016/j.technovation.2018.11.001

Cited by 24 publications

(33 citation statements)

References 46 publications

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“…The BIOCOM AG is a specialized information provider for the European biotech industry and publishes its Year and Address book on an annual basis (see https://biocom.de/angebot/specialist-journals-and-books/?lang=en ). The BIOCOM industry directory has become a main source of information for the German biotechnology and, as shown in [1] , provides a sufficing coverage of the German biotech sector. The industry directory contains basic information for active biotech firms and institutions, such as their geographical location, foundation year and number of employees, together with details on their active collaboration partners.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Moreover, in order to account for the interdisciplinary nature of the biotech sector, stated collaboration partners which have not been listed in the initial BIOCOM industry directory are added to the data together with basic information on their geographic location. Due to incomplete information and methodical concerns, foreign collaboration partners of registered biotech actors are excluded from the data (see [1] for further details). The final reprocessed micro data-set contains information on the geographical location and national collaboration activities of 1002 biotech actors in 2005 and 994 in 2009.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…The following three variables are constructed by summing over the underlying micro data: 1) the number of biotech actors per NUTS3 region, 2) the number of intraregional collaborations between actors within each region i and 3) the number of interregional collaboration activities of biotech actors located in two different regions i and j . An illustrative example for data aggregation to the regional level is provided in [1] . Given the dyadic nature of intra- and interregional collaboration activities among the 439 German NUTS3 regions, the data are stored in a 439 × 439 data matrix with the number of intraregional research activities being recorded on the main diagonal of the matrix, while interregional collaboration activities between two regions are stored as off-diagonal matrix entries.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…In a final step, further regional covariates are added to the regionally aggregated data set on research collaborations. This extension enables applied researchers to conduct multivariate analyses, for instance, in order to investigate the regional determinants of network formation within the boundaries of a closed national research network (see, for instance, [1] for the estimation of a gravity-type model of network formation in knowledge networks as well as [2] for a related study analyzing the determinants of a region׳s centrality position in research collaboration networks and its variation over time). Regional covariates have thereby been selected as to represent node properties and the relationship between nodes [9] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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Modeling interregional research collaborations in German biotechnology using industry directory data

Mitze

Strotebeck

2019

Data in Brief

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This article describes a data set to map and model research collaborations in German biotechnology. Underlying micro-data for firms and institutions in the biotech sector together with information on their research collaboration partners have been extracted from a commercial industry directory, the BIOCOM Year and Address book, for 2005 and 2009. The data have been processed and aggregated to the level of NUTS3 regions. This core data set has been linked to regional covariates which measure the regional endowment with biotech-related research capacities, sector-specific S&T policy support and the strength of a region׳s overall local innovation system. The full data set, which is attached to this article, offers applied researchers an alternative source of information for empirical analyses of knowledge flows in research networks and for studying their determinants. Potential fields of application include social network and regression analysis. First empirical results are reported in “Determining factors of interregional research collaboration in Germany׳s biotech network: Capacity, proximity, policy?” (Mitze and Strotebeck, 2018) and “Centrality and get-richer mechanisms in interregional knowledge networks” (Mitze and Strotebeck, 2018).

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Modeling interregional research collaborations in German biotechnology using industry directory data

Mitze

Strotebeck

2019

Data in Brief

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, further encouragement by local and national government is needed on an inter-regional level to overcome the underdevelopment of some Chinese regions. For example, in a German context, Mitze and Strotebeck (2019) show that the stimulus of network and cluster policies, mainly collaborative R&D funding, positively affect inter-regional collaborations. Governments could cultivate regional interpersonal networks by encouraging inter-regional mobility and subsidizing joint research and development projects or joint regional conferences (Gao et al 2011).…”

Section: Policy and Inter-regional Knowledge Transfermentioning

confidence: 99%

Network dynamics of Chinese university knowledge transfer

Moortel

Crispeels

2019

J Technol Transf

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Social networks are increasingly considered to be influential in explaining the knowledge transfer process. Despite scholarly efforts to integrate knowledge transfer and social network research, we lack understanding on how knowledge transfer networks emerge and evolve. We draw upon resource dependency theory and inter-organizational networks and collect patent data of 42 Double-First Class (DFC) universities to study structural properties of the Chinese university knowledge transfer network over time. Our results point to the existence of an increasingly complex yet remarkably efficient network. Universities and co-patent collaborations emerge in the network and act as knowledge bridges between other universities. The network moves from an early-stage single-centered network to a mature multi-centered network through a power-law pattern. Such movement allows for an aggregation phenomenon to appear as oligopolistic communities emerge and rule the network. While knowledge is more easily shared and accessible within communities, their existence also brings along control over knowledge bases embedded in those communities. Key universities take central positions within the expanding network, which allows them to gain control and easier access to knowledge. It also hints that it might be difficult for other DFC universities to become key players in the network. On an inter-regional level, our findings point to steadily increasing knowledge transfer activity, which is key to overcome the underdevelopment of some Chinese regions. Overall, this paper contributes to our understanding on the theoretical connection between knowledge transfer and social network dynamics, on how universities evolve through knowledge transfer networks, and on how their embeddedness translates into knowledge control, knowledge access, and knowledge bridges.

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Evolution of Two Industrial Districts: The Cases of Hsinchu of Taiwan and Suwon of Korea

Wong

2022

Experimental Learning, Inclusive Growth and Industrialised Economies in Asia

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Determining factors of interregional research collaboration in Germany's biotech network: Capacity, proximity, policy?

Cited by 24 publications

References 46 publications

Modeling interregional research collaborations in German biotechnology using industry directory data

Modeling interregional research collaborations in German biotechnology using industry directory data

Network dynamics of Chinese university knowledge transfer

Evolution of Two Industrial Districts: The Cases of Hsinchu of Taiwan and Suwon of Korea

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