Utilizing wearable technology to characterize and facilitate occupant collaborations in flexible workspaces

TL;DR

This study leverages wearable smartwatch technology and micro-surveys to identify and analyze spontaneous occupant interactions in hybrid workspaces, aiming to optimize space utilization and foster positive work culture.

Contribution

It introduces a novel, non-intrusive method combining real-time micro-surveys and sensor data to study occupant interactions in hybrid work environments.

Findings

Interactions are evenly split among collaboration, socialization, and distraction.

Most interactions occur at occupants' desks with coworkers.

Temporal and spatial factors influence the likelihood of spontaneous interactions.

Abstract

Hybrid working strategies have become, and will continue to be, the norm for many offices. This raises two considerations: newly unoccupied spaces needlessly consume energy, and the occupied spaces need to be effectively used to facilitate meaningful interactions and create a positive, sustainable work culture. This work aims to determine when spontaneous, collaborative interactions occur within the building and the environmental factors that facilitate such interactions. This study uses smartwatch-based micro-surveys using the Cozie platform to identify the occurrence of and spatially place interactions while categorizing them as a collaboration or distraction. This method uniquely circumvents pitfalls associated with surveying and qualitative data collection: occupant behaviors are identified in real-time in a non-intrusive manner, and survey data is corroborated with quantitative sensor data. A proof-of-concept study was deployed with nine hybrid-working participants providing 100 micro-survey cluster responses over approximately two weeks. The results show the spontaneous interactions occurring in hybrid mode are split evenly among the categories of collaboration, wanted socialization, and distraction and primarily occur with coworkers at one's desk. From these data, we can establish various correlations between the occurrence of positive spontaneous interactions and different factors, such as the time of day and the locations in the building. This framework and first deployment provide the foundation for future large-scale data collection experiments and human interaction modeling.