Understanding "Influence": An Empirical Test of the Data-Frame Theory of Sensemaking

This paper reports findings from a study designed to gain broader understanding of sensemaking activities using the Data/Frame Theory as the analytical framework. Although this theory is one of the dominant models of sensemaking, it has not been extensively tested with a range of sensemaking tasks. The tasks discussed here focused on making sense of structures rather than processes or narratives. Eleven researchers were asked to construct understanding of how a scientific community in a particular domain is organized (e.g., people, relationships, contributions, factors) by exploring the concept of "influence" in academia. This topic was chosen because, although researchers frequently handle this type of task, it is unlikely that they have explicitly sought this type of information. We conducted a think-aloud study and semistructured interviews with junior and senior researchers from the human-computer interaction (HCI) domain, asking them to identify current leaders and rising stars in both HCI and chemistry. Data were coded and analyzed using the Data/Frame Model to both test and extend the model. Three themes emerged from the analysis: novices and experts' sensemaking activity chains, constructing frames through indicators, and characteristics of structure tasks. We propose extensions to the Data/Frame Model to accommodate structure sensemaking.