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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27756
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| Title: | Mining Face-to-Face Interaction Networks Using Sociometric Badges:
Predicting Productivity in an IT Configuration Task |
| Authors: | Wu, Lynn
Waber, Ben
Aral, Sinan
Brynjolfsson, Erik
Pentland, Alex |
| Keywords: | Social Networks
Face-to-Face Communications
Information Worker
Productivity |
| Issue Date: | 10-Nov-2008 |
| Series/Report no.: | CeDER-PP-2008-05 |
| Abstract: | Social network theories (e.g. Granovetter 1973, Burt 1992) and
information richness theory (Daft & Lengel 1987) have both been used
independently to understand knowledge transfer in information intensive
work settings. Social network theories explain how network structures
covary with the diffusion and distribution of information, but largely
ignore characteristics of the communication channels (or media) through
which information and knowledge are transferred. Information richness
theory on the other hand focuses explicitly on the communication channel
requirements for different types of knowledge transfer but ignores the
population level topology through which information is transferred in a
network. This paper aims to bridge these two sets of theories to
understand what types of social structures are most conducive to
transferring knowledge and improving work performance in face-toface
communication networks. Using a novel set of data collection tools,
techniques and methodologies, we were able to record precise data on the
face-to-face interaction networks, tonal conversational variation and
physical proximity of a group of IT configuration specialists over a one
month period while they conducted their work. Linking these data to
detailed performance and productivity metrics, we find four main
results. First, the face-toface communication networks of productive
workers display very different topological structures compared to those
discovered for email networks in previous research. In face-to-face
networks, network cohesion is positively correlated with higher worker
productivity, while the opposite is true in email communication. Second,
network cohesion in face-to-face networks is associated with even higher
work performance when executing complex tasks. This result suggests that
network cohesion may complement information-rich communication media for
transferring the complex or tacit knowledge needed to complete complex
tasks. Third, the most effective network structures for
“latent” social networks (those that characterize the
network of available communication partners) differ from
“intask” social networks (those that characterize the
network of communication partners that are actualized during the
execution of a particular task). Finally, the effect of cohesion is much
stronger in face-to-face networks than in physical proximity networks,
demonstrating that information flows in actual conversations (rather
than mere physical proximity) are driving our results. Our work bridges
two influential bodies of research in order to contrast face-to-face
network structure with network structure in electronic communication. We
also contribute a novel set of tools and techniques for discovering and
recording precise face-to-face interaction data in real world work settings. |
| URI: | http://hdl.handle.net/2451/27756 |
| Appears in Collections: | CeDER Published Papers
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