Making sense of smart features in the smart office: a stated choice experiment of office user preferences

Smart office concepts are gaining increasing attention in the built environment domain, as they promise efficient and effective workplaces that cater to user needs. While previous researchers have primarily focused on Internet of Things (IoT) technologies in the office environment and their impacts on office users, few studies have addressed the users' preferences of smart office concepts. This study aims to fill this gap by investigating users' preferences for four smart features: smart parking, smart workspace booking, smart temperature control and smart lighting, and exploring how different attributes of these smart features influence users' preferences. The study conducted a stated choice experiment with 137 valid respondents and analysed the data using both Multinomial Logit (MNL) models and Latent Class (LC) models. The MNL model results indicate that users have varying preferences for different smart features and that there are significant interactive impacts between attribute levels and age, gender, working hours per week and attitudes towards technologies, highlighting the heterogeneity in users' preferences. The LC model revealed two distinct user classes for each smart feature, which have significantly improved the overall model performance. This study guides the design and implementation of smart office concepts that cater to users' needs and preferences.