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Related Experiment Video

Updated: Jun 26, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

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Sensible organizations: technology and methodology for automatically measuring organizational behavior.

Daniel Olguin Olguin¹, Benjamin N Waber, Taemie Kim

¹Human Dynamics Research Group, Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dolguin@media.mit.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 20, 2009

Summary

Wearable sensors captured employee interactions, revealing communication patterns predict job satisfaction and group dynamics. Increased physical proximity correlated with less email, impacting social network analysis.

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Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

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Published on: January 16, 2020

Area of Science:

Organizational behavior
Human-computer interaction
Social network analysis

Background:

Understanding human behavior in organizations is crucial for productivity and satisfaction.
Traditional methods often rely on self-reporting, which can be subjective.
Objective, large-scale data collection on behavior is needed.

Purpose of the Study:

To design, implement, and deploy a wearable computing platform for measuring human behavior in organizational settings.
To automatically capture face-to-face interaction, conversational time, physical proximity, and activity levels.
To understand how behavioral patterns influence individuals and organizations.

Main Methods:

Developed wearable electronic badges equipped with on-body sensors.
Deployed the platform on 22 employees in a real organization for one month.
Combined sensor data with email communication data for analysis.

Main Results:

Successfully predicted employees' job satisfaction and perceptions of group interaction quality.
Total communication volume was a significant predictor for both assessments.
Betweenness centrality in social networks negatively correlated with group interaction satisfaction.
Physical proximity and email exchange showed a significant negative correlation (r = -0.55, p < 0.01).

Conclusions:

Wearable computing platforms can objectively measure and quantify social interactions and organizational dynamics.
Behavioral data from wearable sensors, combined with communication data, offers valuable insights into employee well-being and group functioning.
Findings have significant implications for social network research and understanding workplace dynamics.