Using Social Network Analysis to Analyze Relationships Among IS Journals

2009 | Journal of the Association for Information Systems | Citations: 44

Authors: Polites, Greta L.; Watson, Richard T.

Abstract: Social network analysis (SNA) offers a richer and more objective way of examinin ... Expand

Abstract: Social network analysis (SNA) offers a richer and more objective way of examining individual journal influence and relationships among journals than studies based on individual perceptions, since it avoids personal biases. This article demonstrates how SNA can be used to study the nature of the IS discipline, by presenting results from an exploratory SNA of 125 previously ranked journals from IS and allied disciplines. While many of the most prominent journals in the network are still associated with IS's foundational disciplines, we identify several IS journals that play important roles in disseminating information throughout different subcomponents of the network. We also identify related groups of journals based not only on patterns of information flow, but also on similarity in citation patterns. This enables us to identify the core set of journals that is important for "pure IS" research, as well as other subsets of journals that are important for specialty areas of interest. Overall, results indicate that the IS discipline is still somewhat fragmented and is still a net receiver, as opposed to a net provider, of information from allied disciplines. Like other forms of analysis, SNA is not entirely free from biases. However, these biases can be systematically researched in order to develop an improved, consistent tool with which to examine the IS field via citations among member journals. Thus, while many challenges remain in applying SNA techniques to the study of IS journals, the opportunity to track trends in the discipline over time, with a larger basket of journals, suggests a number of valuable future applications of SNA for understanding the IS publication system. Collapse

Topics: IS discipline reference discipline human computer interaction usability artificial intelligence

Methods: hierarchical clustering sensitivity analysis journal ranking co-citation analysis Bonferroni correction