Network structure and the diffusion of knowledge

Cowan, Robin; Jonard, Nicolas

doi:10.1016/j.jedc.2003.04.002

Cited by 757 publications

(543 citation statements)

References 39 publications

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“…Geographical and technological proximity may induce a process of knowledge diffusion that does not depend directly on economic agents' decisions. In the case of intended spillovers, knowledge flows across a-spatial networks where agents exchange ideas on a voluntary base thanks to formal or informal agreements (Cowan and Jonard, 2004).…”

Section: Introductionmentioning

confidence: 99%

The Complementary Effects of Proximity Dimensions on Knowledge Spillovers

Paci¹,

Marrocu²,

Usaï³

2014

Spatial Economic Analysis

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in WORKING PAPERS2 0 1 1 / 2 1 C O N T R I B U T I D I R I C E R C A C R E N O S C E N T R O R I C E R C H E E C O N O M I C H E N O R D S U D ( C R E N O S ) U N I V E R S I T À D I C A G L I A R I U N I V E R S I T À D I S A S S A R IC R E N O S w a s s e t u p i n 1 9 9 3 w i t h t h e p u r p o s e o f o r g a n i s i n g t h e j o i n t r e s e a r c h e f f o r t o f e c o n o m i s t s f r o m t h e t w o S a r d i n i a n u n i v e r s i t i e s ( C a g l i a r i a n d S a s s a r i ) i n v e s t i g a t i n g d u a l i s m a t t h e i n t e r n a t i o n a l a n d r e g i o n a l l e v e l . C R E N o S ' p r i m a r y a i m i s t o i m p r o v e k n o w l e d g e o n t h e e c o n o m i c g a p b e t w e e n a r e a s a n d t o p r o v i d e u s e f u l i n f o r m a t i o n f o r p o l i c y i n t e r v e n t i o n . P a r t i c u l a r a t t e n ti o n i s p a i d t o t h e r o l e o f i n s t i t u t i o n s , t e c h n o l o g i c a l p r o g r e s s a n d d i f f u s i o n o f i n n o v a t i o n i n t h e p r o c e s s o f c o n v e r g e n c e o r d i v e r g e n c e b e t w e e n e c o n o m i c a r e a s . T o c a r r y o u t i t s r e s e a r c h , C R E N o S c o l l a b o r a t e s w i t h r e s e a r c h c e n t r e s a n d u n i v e r s i t i e s a t b o t h n a t i o n a l a n d i n t e r n a t i o n a l l e v e l . T h e c e n t r e i s a l s o a c t i v e i n t h e f i e l d o f s c i e n t i f i c d i s s e m i n a t i o n , o r g a n i z i n g c o n f e r e n c e s a n d w o r k s h o p s a l o n g w i t h o t h e r a c t i v i t i e s s u c h a s s e m i n a r s a n d s u m m e r s c h o o l s . C R E N o S c r e a t e s a n d m a n a g e s s e v e r a l d a t a b a s e s o f v a r i o u s s o c i o -e c o n o m i c v a r i a b l e s o n I t a l y a n d S a r d i n i a . A t t h e l o c a l l e v e l , C R E N o S p r o m o t e s a n d p a r t i c i p a t e s t o p r o j e c t s i m p a c t i n g o n t h e m o s t r e l e v a n t i s s u e s i n t h e S a r d i n i a n e c o n o m y ,s u c h a s t o u r i s m , e n v i r o n m e n t , t r a n s p o r t s a n d T i t l e : T H E C O M P L E M E N T A R Y E F F E C T S O F P R O X I M I T Y D I M E N S I O N S O University of Cagliari and CRENoSAbstract The purpose of this paper is to analyse the effect of various proximity dimensions on the innovative capacity of 276 regions in Europe within a knowledge production function model, where R&D and human ...

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Section: Introductionmentioning

confidence: 99%

The Complementary Effects of Proximity Dimensions on Knowledge Spillovers

Paci¹,

Marrocu²,

Usaï³

2014

Spatial Economic Analysis

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“…However, more centralized networks are also more vulnerable, since their dependence on the functioning of single actors is higher as compared to other network structures. With respect to the rate of knowledge diffusion, Cowan & Jonard (2004) could show that small world structures are the superior form of organization. The results of Schilling & Phelps (2007) on the structure of industry networks add to the difficulties in evaluating this development towards increased centralization.…”

Section: Resultsmentioning

confidence: 99%

Policy Induced Innovation Networks: The Case of the German “Leading-Edge Cluster Competition”

Cantner

Graf

Hinzmann

2013

The Geography of Networks and R&D Collaborations

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe last decades saw a pronounced shift in innovation policy in Germany and many other countries towards increased funding of cooperative R&D. Over the last years, competitions between regional initiatives pushed this trend even further by adding a regional perspective, by increasing the scope of funding, and by fostering interaction between a large number of actors. In 2008 the German ministry for education and research (BMBF) started the "Leading-Edge Cluster competition" ("Spitzencluster-Wettbewerb") in which 15 clusters were selected in three waves (2008, 2010, 2012) and are funded for a five-year period with up to 40 million Euro each. Our paper presents selected results regarding the influence of government funding on cooperation networks within five of the clusters that were successful in the first wave of the "SpitzenclusterWettbewerb". More specifically, we analyse the extent of policy influence on the network of most important cooperation partners, its geographic reach, and the changes of network structure in general. Our empirical analysis is based on original data that was collected in 2011 with cluster actors (firms and public research) who received government funding. Our results indicate that the program was quite effective in initiating new cooperations between cluster actors and in intensifying existing linkages. The vast majority of the linkages which are influenced by the cluster competition are between actors located in the cluster region. With respect to the influence of the cluster competition on network structure, we find an increase in network centralization. Small and medium sized enterprises used the chance to connect with the local 'stars', but not as much among each other.

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“…Cowan (2004) examined the relationship between network structure and diffusion performance, and concluded that knowledge diffusion in small-world networks with different structure properties have great difference [9]. These studies have used simulation to explore knowledge diffusion process, but did not analyze the impact of such knowledge diffusion on the overall economy of high-tech innovation networks.…”

Section: Introductionmentioning

confidence: 99%

Value Model of Knowledge Diffusion in High Technology Innovation Networks

Xiao

Han

2014

Service Science and Knowledge Innovation

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Part 6: Decision Making and Knowledge ManagementInternational audienceTo measure the influence of knowledge diffusion and information exchange between enterprises on high-tech innovation networks’ production and operation, a production model and innovation model based on a network diffusion process are introduced. The knowledge diffusion process treated as an learning-by-observing process in a random network are influenced both by network’s structure and non-structure properties. We analyze the influence of diffusion process theoretically and find that, if given the precondition that initial belief and belief elasticity follow a normal distribution, an increase in mean of initial belief would lead to increase in PV and IV; otherwise, mean of belief elasticity would have a opposite effect on PV and IV under some different conditions. Finally, we give the condition to compare knowledge diffusions in two high-tech networks with same mean degree but different variance

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Network structure and the diffusion of knowledge

Cited by 757 publications

References 39 publications

The Complementary Effects of Proximity Dimensions on Knowledge Spillovers

The Complementary Effects of Proximity Dimensions on Knowledge Spillovers

Policy Induced Innovation Networks: The Case of the German “Leading-Edge Cluster Competition”

Value Model of Knowledge Diffusion in High Technology Innovation Networks

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