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Smart thermostats differ significantly from traditional devices and are quickly becoming commonplace in homes. Literature demonstrates that thermostat interfaces greatly influence user interaction and related energy outcomes. Moreover, how users imagine their device to work appears to have a greater impact on usage than how the system functions. Previous work investigated manual and programmable thermostats in this context, employing user mental models (UMMs) to analyse user understanding. Since then, thermostats have developed significantly. This paper presents a novel investigation of smart thermostat UMMs. It employs contemporary methods to construct ten UMM diagrams, and three detailed case studies, contextualized with previous findings. All participants demonstrated feedback theory. Case studies highlight common misconceptions. Overall, smart thermostat UMMs appear to enable effective usage; however, some users are overwhelmed by the complexity, limiting engagement and use of features (e.g. programming).

Evolving interaction: a qualitative investigation of user mental models for smart thermostat users