David Katerndahl1. 1. Department of Family and Community Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229-3900, USA. katerndahl@uthscsa.edu
Abstract
RATIONALE: Understanding scientific collaboration networks can assist research centres to develop strategies to maximize productivity, and help diagnose the causes of low system productivity. The purpose of this study was to use social network analysis to better understand how research collaboration within a productive department impacts scholarly productivity individually and departmentally. METHODS: Over a 13-year period, departmental faculty completed an annual survey describing their research collaborations and scholarly productivity. Data were analyzed using social network analysis. Quadratic assignment procedure regression assessed the predictive value that an individual's measures of centrality within the network and effective size of their own network (egonet) had each year in predicting each scholarly outcome. Simulation Investigation for Empirical Network Analysis software assessed the co-evolution of the collaborative network and scholarship. RESULTS: While no consistent patterns for individual's presentations were seen, individual's publications were associated with betweenness and eigenvector centrality, and effective egonet size. Grant submissions were associated with degree and eigenvector centrality, as well as effective egonet size. Departmentally, network dynamics depended upon the scholarship of those around you, but none of the forms of scholarship depended upon network characteristics. Of the three forms of scholarship, network dynamics depended primarily on publications in others. CONCLUSION: Although individual scholarship was dependent upon individual centrality and effective egonet size, research collaboration within the department depended upon reciprocity, transitivity and scholarly productivity of its constituent investigators. Scholarly dynamics, at a departmental level, did not depend upon network characteristics.
RATIONALE: Understanding scientific collaboration networks can assist research centres to develop strategies to maximize productivity, and help diagnose the causes of low system productivity. The purpose of this study was to use social network analysis to better understand how research collaboration within a productive department impacts scholarly productivity individually and departmentally. METHODS: Over a 13-year period, departmental faculty completed an annual survey describing their research collaborations and scholarly productivity. Data were analyzed using social network analysis. Quadratic assignment procedure regression assessed the predictive value that an individual's measures of centrality within the network and effective size of their own network (egonet) had each year in predicting each scholarly outcome. Simulation Investigation for Empirical Network Analysis software assessed the co-evolution of the collaborative network and scholarship. RESULTS: While no consistent patterns for individual's presentations were seen, individual's publications were associated with betweenness and eigenvector centrality, and effective egonet size. Grant submissions were associated with degree and eigenvector centrality, as well as effective egonet size. Departmentally, network dynamics depended upon the scholarship of those around you, but none of the forms of scholarship depended upon network characteristics. Of the three forms of scholarship, network dynamics depended primarily on publications in others. CONCLUSION: Although individual scholarship was dependent upon individual centrality and effective egonet size, research collaboration within the department depended upon reciprocity, transitivity and scholarly productivity of its constituent investigators. Scholarly dynamics, at a departmental level, did not depend upon network characteristics.