Abstract

In contemporary knowledge-intensive spaces, workers often team with experts from different disciplinary backgrounds and different geographic locations and, thus, they face the challenge of integrating knowledge in their work. When modeling how communication can be improved in these circumstances, previous studies have often relied on social network analysis to understand the aggregate exchanges among team members. In this study, rather than analyze social networks (people linked by communication), we argue that network analysis of knowledge networks (people linked by common knowledge) presents an opportunity to better understand and address the challenge of knowledge integration in organizational contexts. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</b> 1. How can professional communicators use the distribution of knowledge on teams as a structure for planning interventions in the work of complex, collaborative teams? 2. What kinds of insights do networks of specific knowledge areas offer professional communicators about team communication challenges? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> We describe prior uses of network analysis in professional communication research that inform our development of a knowledge network. In particular, we review current literature and highlight network-based concepts that we believe are organizing principles of knowledge networks. Previous literature has shown that network models, particularly social network models, are useful tools for professional communication researchers to examine a range of communication factors and practices. However, professional communication research has yet to fully explore the possible contributions of knowledge networks to understand communication processes. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</b> We conducted an ethnography of a team science collaboration and used observations to create a survey of terms that measured subjects’ self-professed understanding of key concepts. We used the survey results to produce a bimodal network model of agents and terms, in which we binarized link values after filtering for only the highest-rated terms for each subject. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> The model demonstrated that the team collaboration broke into two distinct groupings. Ego networks extracted from this parent network showed that concepts commonly well-understood in the team join together multiple subgroups of expert knowledge. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</b> The knowledge network is a useful instrument in helping team members understand possibilities for integrating knowledge across disciplines and subspecialties. The visual produced by this model also can be useful for developing research questions and strategizing work processes.