Studying Scientific Collaboration Networks Using Social Network Analysis And Structural Equation Modeling
The main objective of this study was to define a conceptual model to identify latent issues that involve the scientific collaboration in a R&D environment. We have used two complementary approaches to study scientific collaboration. At first, structural equation modeling with partial least squares was used to evaluate and test a conceptual model based on personal, behavioral, cultural and circumstantial factors to identify which of these factors best explain the propensity of authors of technical and scientific publications to establish collaboration links with each other. The first part produced a second order latent variable named “propensity to collaborate”. In the second part, we evaluate if and how this propensity to collaborate is reflected in the structural position of these authors in the R&D coauthorship network of our case study, . The findings showed that the proposed factors moderately explain the authors’ collaboration propensity in a R&D environment. Although the model has not been satisfactory to explain the authors’ structural position in the coauthorship network, however, it was a starting point to study scientific collaboration using structural equation modeling and social network analysis.
Citação
@online{carlos_anisio2024,
author = {Carlos Anisio , Monteiro and Menezes, Mario Olimpio, de and
Carlos, O, Antonio},
title = {Studying Scientific Collaboration Networks Using Social
Network Analysis And Structural Equation Modeling},
volume = {29},
number = {3},
date = {2024-03-01},
doi = {10.9790/0837-2903045870},
langid = {pt-BR},
abstract = {The main objective of this study was to define a
conceptual model to identify latent issues that involve the
scientific collaboration in a R\&D environment. We have used two
complementary approaches to study scientific collaboration. At
first, structural equation modeling with partial least squares was
used to evaluate and test a conceptual model based on personal,
behavioral, cultural and circumstantial factors to identify which of
these factors best explain the propensity of authors of technical
and scientific publications to establish collaboration links with
each other. The first part produced a second order latent variable
named “propensity to collaborate”. In the second part, we evaluate
if and how this propensity to collaborate is reflected in the
structural position of these authors in the R\&D coauthorship
network of our case study, . The findings showed that the proposed
factors moderately explain the authors’ collaboration propensity in
a R\&D environment. Although the model has not been satisfactory to
explain the authors’ structural position in the coauthorship
network, however, it was a starting point to study scientific
collaboration using structural equation modeling and social network
analysis.}
}