What connections lead to good scientific performance?

Jing Tu ()
Additional contact information
Jing Tu: Wuhan University of Science and Technology

Scientometrics, 2019, vol. 118, issue 2, No 10, 587-604

Abstract: Abstract This paper concentrates on the connections in the collaboration network and aims to explore what kinds of connections improve the joint output of the two nodes in connection, using the collaboration data of top institutions in the field of Information Science and Library Science for the period 2007–2016. More intensive connections are found between top institutions, and most institutions are connected into the largest component. The effect of international connection on performance is compared between US and non-US institutions. The homophily of centrality, tie strength and h-index measured as assortativity coefficient is described to show how institutions of similar properties tend to connect with each other in the graph. Furtherly, a negative binomial regression model is employed to investigate the relationship between the homogenous connections and the citation counts received by the connections. Characteristics of connections that contribute to good performance are then obtained.

Keywords: Connections; Inter-institutional collaboration; Structural homophily; Performance (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-018-02997-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:118:y:2019:i:2:d:10.1007_s11192-018-02997-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-018-02997-7

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().