Harnessing Social Network Analysis in Knowledge and Ignorance Mobilization Research and Evaluation / Intégrer l’analyse de réseau dans la recherche et l’évaluation de la mobilisation de la connaissance et de l’ignorance
In this guest post Joanne Gaudet (UOttawa) explores the application of social network analysis (SNA) to knowledge mobilization. While this is not new per se, her use of SNA as mapping the relationship between knowledge mobilization people and knowledge mobilization products is new. This use of SNA captures a more realistic view of a knowledge mobilization network by connecting it to not only those people engaging in knowledge mobilization but also the products and events that broker those connections. You can see more from Joanne Gaudet at her website http://ignorancemobilization.com
Notre bloggeuse invitée, Joanne Gaudet (uOttawa), explore l’utilisation de l’analyse de réseau afin de mieux comprendre la mobilisation de la connaissance. Bien que ce type d’analyse ne soit pas nouveau, l’utilisation de l’analyse de réseau comme cartographie des liens entre les individus qui mobilisent la connaissance et les produits de la mobilisation de la connaissance est une idée novatrice. Cette façon d’exploiter l’analyse de réseau capte une perspective plus réaliste des réseaux de la mobilisation de la connaissance en incorporant non seulement les individus qui mobilisent la connaissance, mais aussi les produits et les événements qui tissent les liens entre ces individus. Apprenez-en plus sur la recherche de Joanne Gaudet en visitant son site web, http://ignorancemobilization.com
When I first contemplated using social network analysis (SNA) in a knowledge and ignorance mobilization (KM/IM) research project, I was astounded that most research limited itself to peer reviewed publications and their citations, while some considered research projects. The more I looked into the potential of SNA for KM/IM, the more it became clear that performing SNA on peer reviewed publications, their citations, and research projects would mostly limit understanding for potential KM/IM and impact within academia.
In addition, relations in the social networks generated above frequently embedded assumptions about how individuals relate with each other. For example, it was assumed that if a student was listed as author, the student had engaged in relation with all other co-authors. It was also assumed that all researchers listed as collaborators in a research project engaged in relation. Yet, typically, not all co-authors or collaborators engage in KM/IM with other co-authors and collaborators, and students relate with their supervisor, not other authors or collaborators.
What I briefly explore in this post is an SNA approach I developed to deal with the limitations above (and a few more…). I share it here with a goal of planting a network seed in your KM/IM research and evaluation garden!
In a nutshell, social network analysis refers to the analysis of relations among actors (i.e., human and non-human such as organizations, peer reviewed papers, citations) and of structural properties for the web of relations that bind them together. All the links in a given network represent the same type of relation (i.e., friendship, professional communication, or knowledge mobilization). Network maps make actors and their relations visible and can help gauge how KM/IM flows (or not), identify key actors or clusters, and highlight changes in actor KM/IM over time.
I propose a ‘mobilization-network’ approach for the SNA of KM/IM. The underlying logic of the approach incorporates and builds on publication and citation SNA research. The approach applies to ego networks (also known as personal networks based on a specific actor) and can extend to whole networks. An ego is a core actor for which you investigate KM/IM relations. For example, an ego can be a laboratory, a research group, or an organization that is part of a wider network.
To start, investigating KM/IM using the mobilization-network approach means identifying mobilization actors. Mobilization actors, unsurprisingly, mobilize explicit and tacit knowledge produced and co-produced by the ego. The ego itself is therefore also a mobilization actor where a research group or laboratory’s institutional attributes contribute to mobilization potential. Mobilization actors come in many shapes and sizes, and will vary depending on your ego or whole network because these are not pre-determined, they are investigated as they emerge. Examples of mobilization actors include peer-reviewed papers, research projects, blog posts, policies, media events (oral, written, audio-visual), business ventures, laboratories, research groups, conference presentations, training, apprenticeships, non-peer reviewed publications, websites, and town hall meetings.
Notice that all mobilization actors are non-human, this helps do away with the types of assumptions described above – humans can relate with mobilization actors, but do not expressly create links between mobilization actors. What brings humans together for KM/IM is the mobilization actor acting as the ‘glue that binds’ humans for short or long periods of time. Linked with mobilization actors can be a myriad of other actors including other mobilization actors, individuals or organizations. These include funders, co-authors, policymakers, students, stakeholders, researchers, citing papers, specific groups or communities, conference attendees, responses on blog posts, spin-off social programs, and other websites.
I illustrate with a minimalist simulated KM/IM network. A research group as ego (holding KM/IM relations with 2 funders and 3 researchers) can hold a KM/IM tie with a peer reviewed paper (in relation with three authors, one of which is a research group member, and a journal) as portrayed in Figure 1 (ego circled in red). The paper mobilizes knowledge (and ignorance) among co-authors and then holds potential for mobilization of the content of the paper by other mobilization actors. Here KM/IM is through citation in another paper, use in a policy, and implementation in a community pilot project (with a tie to one of the same funders as the ego) as in Figure 2. Ties go beyond academia where we capture rich relational details for KM/IM.
The mobilization-network approach is cumulative in order to capture potential for future KM/IM. I refer to an empirical case in this second example. On the left-hand of Figure 3 is a map for the 1st year of a basic research laboratory (ego circled in green) as it started out in the PrioNet Canada Network of Centres of Excellence whereas the map on the right-hand is the 7th year of the laboratory’s KM/IM in PrioNet. The social network map in the first year had 10 actors and 14 ties and in the 7th year, it had 366 actors with 750 KM/IM ties. In its final year, a bibliometric collaboration analysis would have only accounted for 4% of the mobilization-network, and a bibliometric citation analysis would have represented only 11% of the mobilization-network. Finally, by combining SNA with interview data in the empirical case, I was able to confirm ties between funded research projects and specific published papers, training, and other mobilization actors. Such fine-grained KM/IM relational data can be valuable for policymakers and funding agencies to understand KM/IM longitudinally.
With this quick glimpse into SNA for KM/IM, I hope you see the potential SNA holds when we harness it to understand intricate networks of KM/IM relations built over time. Peering through a network lens provides an opportunity to see KM/IM relations in a whole new way!