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Sensor-based proximity metrics for team research. A validation study across three organizational contexts
Jörg Müller1, Julio Meneses2, Anne Laure Humbert3
1Universitat Oberta de Catalunya, Internet Interdisciplinary Institute IN3, Barcelona, Spain. jmuller@uoc.edu.
Behavior Research Methods
|August 20, 2020
Summary
Bluetooth (BT) sensor data in organizational research shows strong correlations with self-reported social ties. However, context matters: advice-seeking ties are best detected in universities, while friendships are found at greater distances in research labs.
Area of Science:
- Organizational Behavior
- Human-Computer Interaction
- Social Network Analysis
Background:
- Wearable sensors are increasingly used in organizational research.
- Existing validation studies are often limited to lab settings or single field cases.
- Contextual factors' influence on sensor measurements is understudied.
Purpose of the Study:
- To validate Bluetooth (BT) detections for expressive and instrumental ties in real-world organizational settings.
- To examine the convergent validity of BT detections across different organizational contexts.
- To investigate how organizational context influences sensor-based social tie measurements.
Main Methods:
- A validation study involving nine R&D teams.
- Convergent validity analysis of Bluetooth (BT) detections against self-reported friendship and advice-seeking ties.
- Comparison across three organizational contexts: research labs, private companies, and university-based teams.
Main Results:
- Bluetooth (BT) detections generally showed strong correlations with self-reported social ties.
- Organizational context significantly affected the strength and direction of these correlations.
- Advice-seeking ties were best identified in university settings (close proximity), while friendships were detected at greater distances, especially in research labs.
Conclusions:
- Sensor-based measurements, like BT detections, can be valid for studying social ties in organizations.
- Organizational context is a critical factor influencing the accuracy and interpretation of sensor data.
- Recommendations include careful organizational embedding and mixed-methods research for enhanced sensor validity.

