Colloquium: John Hollenbeck, Michigan State University / USA

Date: Tuesday, January 29, 2019

Time: 13:30

Location: room 5419-0009 (Kapteynborg)

Title:

Within-Person Variability in Network Positioning: Toward a Theory of Adaptive Embeddedness

Abstract:

The position one holds in a social network has strong implications for the person’s ability to perform various tasks and adjust socially. A person can be permanently under-embedded or over-embedded or adaptively move their positioning within and between networks as needed over time. Unfortunately, the time consuming nature of capturing social network data has prevented researchers from examining within-person variability in such positioning (i.e., granular changes in position over short time periods). This in turn, has limited our ability to study the temporal contingencies associated with how some individuals adaptively move in and out of networks, whereas other individuals become permanently isolated or permanently anchored in small, limiting cliques. In fact, researchers have largely ignored task and network dynamics that unfold on a daily scale because they have not been able to develop granular descriptions of daily interaction patterns. 

Fortunately, developments in the field of wearable sensors present revolutionary opportunities for assessing interaction patterns in an efficient and non-obtrusive manner. Every day, wearable sensors become more diverse, less intrusive, and more affordable. In particular, recent developments in the commoditization of low-energy Bluetooth devices have provided game-changing opportunities to capture the dynamic nature of social networks on a daily or hourly basis. Research has documented wide variability in “daily social networks” captured via Bluetooth signals – variance that is totally lost when network data are aggregated over extended time-periods. This within-person variability in network positioning has important implications for individuals and organizations, and the objective of the research presented here is to develop and test a theory of adaptive embeddedness.