  Most people spend up to 90 % of their time indoors. However, literature in
the field of facility management and related disciplines mostly focus on energy
and cost saving aspects of buildings. Especially in the area of commercial
buildings, only few articles take a user-centric perspective and none of them
considers the subjectivity of thermal comfort. This work addresses this
research gap and aims to optimize individual environmental comfort in open
office environments, taking advantage of changes in modern office
infrastructure and considering actual user feedback without interfering with
existing systems. Based on a Design Science Research approach, we first perform
a user experience testing in an exemplary corporate office building.
Furthermore, we build a mechanism to gather user feedback on environmental
comfort. Based on this, we build a machine learning model including different
IoT data sources (e.g. building data and weather data) with an average
coefficient of determination of 41.5%. Using these insights, we are able to
suggest current individual comfort zones within the building and help employees
to make better informed decisions on where to sit or what to wear, to feel
comfortable and work productively. Therefore, we contribute to the body of
knowledge by proposing a user-centric design within a cross-disciplinary
context on the basis of analytical processes.
