Abstract

http://ssrn.com/abstract=1130251
 
 

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Mining Face-to-Face Interaction Networks using Sociometric Badges: Predicting Productivity in an IT Configuration Task


Lynn Wu


University of Pennsylvania - The Wharton School; Massachusetts Institute of Technology (MIT)

Benjamin N. Waber


MIT - Media Laboratory

Sinan Aral


Massachusetts Institute of Technology (MIT) - Sloan School of Management

Erik Brynjolfsson


Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER)

Alex Pentland


Massachusetts Institute of Technology (MIT)

May 7, 2008


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-to-face 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-to-face 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 in-task 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.

Number of Pages in PDF File: 19

Keywords: Social Networks, Face-to-Face Communication, Information Worker, Productivity

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Date posted: May 8, 2008  

Suggested Citation

Wu, Lynn and Waber, Benjamin N. and Aral, Sinan and Brynjolfsson, Erik and Pentland, Alex, Mining Face-to-Face Interaction Networks using Sociometric Badges: Predicting Productivity in an IT Configuration Task (May 7, 2008). Available at SSRN: http://ssrn.com/abstract=1130251 or http://dx.doi.org/10.2139/ssrn.1130251

Contact Information

Lynn Wu
University of Pennsylvania - The Wharton School ( email )
3733 Spruce Street
Philadelphia, PA 19104-6374
United States
Massachusetts Institute of Technology (MIT) ( email )
77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States
Benjamin N. Waber
MIT - Media Laboratory ( email )
20 Ames St.
Cambridge, MA 02139-4307
United States
617-253-4662 (Phone)
617-253-6285 (Fax)
HOME PAGE: http://www.media.mit.edu/~bwaber
Sinan Aral (Contact Author)
Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )
77 Massachusetts Ave.
E62-416
Cambridge, MA 02142
United States
Erik Brynjolfsson
Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )
E53-313
Cambridge, MA 02142
United States
617-253-4319 (Phone)
HOME PAGE: http://digital.mit.edu/erik
National Bureau of Economic Research (NBER) ( email )
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Alex Pentland
Massachusetts Institute of Technology (MIT) ( email )
77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States
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References:  59
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