Abstract
Office workers respond to a variety of environmental and contextual stimuli, which can influence their comfort and behaviour. Although recent models address the unpredictability of human–building interactions, they often neglect multi-domain factors and user motivations. A holistic understanding entails environmental, personal and contextual influences. The use of direct occupant feedback can reveal perceptions and drivers of behaviour that physical data alone cannot capture. This study introduces a multi-domain and mixed-method data collection framework for open-plan offices entitled eCOMBINE. This framework integrates both subjective and objective data across domains to better capture the factors influencing occupant behaviour and perception, particularly in buildings with operable windows and blinds. Central to this framework is the ‘OBdrive’ mobile application, which records self-reported motivations for window, blind and light control actions. The framework was implemented in two Swiss case studies, involving 44 participants. Results demonstrate the framework’s feasibility and participant acceptance. Benefits arise from embedding comprehensive data collection within research workflows. The eCOMBINE framework can support future research and inform more effective and efficient building design and operation by investigating user motivations, adaptive actions, health impacts, behaviour-driven simulations and optimal sensor set-ups.
PRACTICE RELEVANCE
Open-plan offices merit specific attention in energy-related behavioural research as their occupants typically have diverse personal needs and preferences that need to be met in a shared environment. The eCOMBINE framework adopts an integrated approach to study the relationships among combined indoor environmental quality, occupant behaviour, global environmental comfort and building energy use. By capturing multi-domain environmental conditions alongside subjective perceptions, motivations and contextual variables, eCOMBINE enables research into human–building interactions and facilitates integrated analysis into post-occupancy evaluation. These insights can support the development of more accurate and human-centred behavioural models, which in turn can help to improve the predictive reliability of building performance simulations. Such models can be used to optimise office building design and enhance both indoor environmental quality and energy performance by considering the synergies and trade-offs across environmental domains, adaptive opportunities, individual preferences and workplace context.