Functional information characteristics of large-scale research collaboration: network measures and implications

Ghosh, Jaideep; Kshitij, Avinash; Kadyan, Sandeep

doi:10.1007/s11192-014-1475-4

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…There are two centrality indicators in social network analysis: degree centrality (referred to as centrality in the following) and betweenness centrality. These two indicators that have been widely used in the measurement of innovation collaboration networks can measure the importance of nodes in a network by calculating the number of nodes connected to them and the distance from other nodes (Ebadi and Schiffauerova, 2015; Ghosh et al , 2015; Muñoz-Muñoz and Mirón-Valdivieso, 2017; Whittington, 2018). Betweenness centrality measures how much the country sits on the shortest path between other pairs of countries in the network.…”

Section: Methodsmentioning

confidence: 99%

The effect of intellectual property treaties on international innovation collaboration: a study based on USPTO patents during 1976–2017

Xiang

Wang

2021

LHT

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PurposeStudies have indicated that international innovation collaboration has promoted technology transfer and knowledge spillover between countries. The conclusion of various international intellectual property (IP) treaties has played an essential role in optimizing the international innovation and collaboration environment. This study investigates the effect of IP treaties on international innovation collaboration and whether international IP treaties can promote collaboration between a country and other economies in the world.Design/methodology/approachAfter collecting and extracting the patent record data from the United States Patent and Trademark Office (USPTO), a final dataset of 3,213,626 cooperative patents and 465,236 pairs of collaborations between two countries or regions is established. Based on the international patent collaboration data of 192 countries during 1976–2017, the changes in patent collaboration indicators after these countries joined 23 IP treaties are analyzed.FindingsInternational IP treaties have significantly increased the number of patent cooperation countries of a country and its importance in international cooperation networks. The role of IP treaties is more manifested by the increased opportunities for a country's international innovation cooperation than its influence on global innovation; this is of extreme significance for developing countries to introduce advanced technologies.Originality/valueGinarte and Park (1997) have confirmed that IP treaties have helped to raise the level of IP protection. In this study, the increase in the degree centrality of the international innovation network is evidence of IP treaties to promote innovation cooperation. For a developing country, joining an intellectual property treaty may strengthen intellectual property protection and optimize its own international innovation cooperation methods.

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Section: Methodsmentioning

confidence: 99%

The effect of intellectual property treaties on international innovation collaboration: a study based on USPTO patents during 1976–2017

Xiang

Wang

2021

LHT

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“…In the same line, Luis Bettencourt and co-authors follow the emergence and development of a series of research specialties tracking several co-authorship network metrics as the corresponding fields evolve (Bettencourt, Kaiser and Kaur 2009). More recently, Ghosh and collaborators followed several structural measures of evolving coauthorship networks (Ghosh, Kshitij and Kadyan 2014) however; their treatment of network cohesion is quite superficial.…”

Section: Social Network Analysis Of Collaborationmentioning

confidence: 99%

Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

Amat

Perruchas

2016

Scientometrics

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“…In recent years, research collaboration studies have explored many important components in collaboration activities such as collaboration patterns (Amjad et al, 2018;D'Ippolito and Rüling, 2019), collaboration strategies (Bozeman and Corley, 2004;Katz and Martin, 1997;Melin, 2000;Hackett, 2005) and collaboration outcomes (Rigby and Edler, 2005;Goddard and Isabelle, 2006) from different levels of collaboration such as individual, group, department, institution, sector and nation (Katz and Martin, 1997) using various methods including ethnographic inquiry (Knorr-Cetina, 1999), comparative case study research (Chompalov et al, 2002), large-scale surveys (Stokes and Hartley, 1989;Ponomariov and Boardman, 2008;Bozeman and Gaughan, 2011), social network analysis (Fleming et al, 2007;Ghosh et al, 2015;Wang and Hicks, 2015) and bibliometric analyses (Glänzel, 2002;Hou et al, 2008).…”

Section: Collaboration In Academic Social Networkmentioning

confidence: 99%

Academic social networks and collaboration patterns

Chen

2019

LHT

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Purpose This study attempts to use a new source of data collection from open government data sets to identify potential academic social networks (ASNs) and defines their collaboration patterns. The purpose of this paper is to propose a direction that may advance our current understanding on how or why ASNs are formed or motivated and influence their research collaboration. Design/methodology/approach This study first reviews the open data sets in Taiwan, which is ranked as the first state in Global Open Data Index published by Open Knowledge Foundation to select the data sets that expose the government’s R&D activities. Then, based on the theory review of research collaboration, potential ASNs in those data sets are identified and are further generalized as various collaboration patterns. A research collaboration framework is used to present these patterns. Findings Project-based social networks, learning-based social networks and institution-based social networks are identified and linked to various collaboration patterns. Their collaboration mechanisms, e.g., team composition, motivation, relationship, measurement, and benefit-cost, are also discussed and compared. Originality/value In traditional, ASNs have usually been known as co-authorship networks or co-inventorship networks due to the limitation of data collection. This study first identifies some ASNs that may be formed before co-authorship networks or co-inventorship networks are formally built-up, and may influence the outcomes of research collaborations. These information allow researchers to deeply dive into the structure of ASNs and resolve collaboration mechanisms.

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Functional information characteristics of large-scale research collaboration: network measures and implications

Cited by 13 publications

References 44 publications

The effect of intellectual property treaties on international innovation collaboration: a study based on USPTO patents during 1976–2017

The effect of intellectual property treaties on international innovation collaboration: a study based on USPTO patents during 1976–2017

Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

Academic social networks and collaboration patterns

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