Intelligent buildings with IoT systems using ML and HITL for indoor environmental control: an investigation of occupants’ adoption intent

Office spaces in Intelligent Buildings are equipped with Building Management Systems (BMSs) that use Internet of Things (IoT) with Machine Learning (ML) for saving energy while improving comfort for occupants. The Human-In-The-Loop (HITL) approach in such IoT-based systems has made it possible to empower occupants to set local zone comfort, and use personalized controls, such as blasts of cold or hot air, and lighting settings. However, prior literature shows that occupants engaging with such IoT-based indoor environmental control systems are uncertain and might pose challenges in commercial buildings. This paper discusses an investigation of factors that influence the intent of occupants to adopt such IoT systems that leverage ML and HITL for indoor environmental control. Customizing and extending the Technology Acceptance Model, this research incorporates additional constructs, namely trust, invasiveness, and energy savings propensity, to examine occupants’ intention to accept and engage with IoT-based BMSs quantitatively using multiple regression. The results suggest that perceived benefits, namely comfort, productivity and wellbeing, along with the perceived ease-of-use aspects, namely ease of zone movement, and convenience to access BMS, significantly influence adoption intent. The perceived risks, namely security, privacy and intrusiveness, are not statistically significant. Further, propensity to save energy negatively influences the relationship between perceived benefits and adoption intent. Based on the results of this study, IoT firms can identify potential early adopters of such IoT systems and services in office spaces, carve out value proposition elements, and take strategic decisions.