An Integrated Methodological Framework to Investigate Hybrid Work Technologies

Julian Fietkau

University of the Bundeswehr Munich

Coauthors: Jan Schwarzer, Michael Koch, Susanne Draheim, Kai von Luck

ECSCW 2025

2nd International Workshop on Hybrid Collaboration – Analyzing Collaborative Interaction

Researching hybrid work: the role of automation

  • Research into work processes in professional contexts traditionally relies on ethnographic methods
  • Can we use body tracking sensors (or other automated means) to gather meaningful long-term insight from such contexts, without requiring continuous researcher presence?
    • This was (more or less) the research question of our project “Investigation of the honeypot effect on (semi-)public interactive ambient displays in long-term field studies” (2021–2024)
    • Answer: well... kind of? 🤔

What do body tracking sensors do anyway?

Two large touch screens in a room with furniture on the side, each screen has an additional black sensor mounted on top of it
Three computer-rendered stylized people (like stick figures) are shown standing together in an flat empty space

Data exploration: walking paths

Two images: a computer rendering of a stylized person walking in a circle, with the walking path so far shown underneath the person on the ground, and the second image is a top-down rendering of just the walking path

Clustering walking paths

Hundreds of walking paths with a visibly higher concentration in the top left of the image, but also lots of chaotic traces all aroundLess chaos, a more discernible arc in the top leftAll walking paths other than the curve along the popular path in the top left of the image have been sorted out
Walking path clustering process from a real deployment study

What have we successfully measured via body tracking data?

  • Walking paths
  • Coffee mug effect ☕
  • Honeypot effect (kind of)

Problem: the “wall of intent” – body tracking data does not show why people do what they do

Re-incorporating qualitative methods

Qualitative dataConsolidation(coding, etc.)DiarystudiesAnalog, hybrid,and digitalactivitiesContextinformationDisplaycontentSettingcharacteristicsAudienceOthersourcesField notes fromobservationsInterviewsFocusgroupsBody tracking and touch interaction data1Datacollection2Preparation3Exploration4Featureextraction5AnalysisSynchronizationHolistic, automatic,and algorithmic interpretations

Conclusions

  • Body tracking data can be a rich source of descriptive/
    observational insight
  • But: observation of movements leaves intentions in the dark
  • We have high hopes for more sophisticated integrations of quantitative (sensor-based) insight with qualitative, ethnographic methods
  • Future progress will depend on pragmatic factors (i.e. funding)

@julian@fietkau.social

https://fietkau.science/methodological_framework_hybrid_work_technologies