Ambient Intelligence Applications in Architecture: Factors Affecting Adoption Decisions

Abstract

Ambient Intelligent systems enabled by recent advancements in Artificial Intelligence (AI) and Internet of Things (IoT) technologies, offer new mechanisms for data-driven decision making in architectural design. Understanding why architects are willing to adopt these systems and what factors affect their decisions is a critical first step towards assessing the viability and informing the design of these systems. We develop and contextualize a theoretical model that examines the impact of architects’ perceptions of value and risk associated with ambient intelligence (AmI) on their intention to adopt these systems during the design phase. The model also examines the role that commitment to learning about AmI and commitment to collaboration with IT professionals play in mediating such impacts. We validate the model via a field survey of architects across the North America. Our findings indicate that both perceived value and perceived risk play varying roles in adopting AmI and that commitment to learning and commitment to collaboration each acts as a mediator between architects’ perceptions and their behavioral intentions. This study presents an attempt to examine the slow adoption rate of AmI in architectural design and thus offers a path to future theoretical developments and practical insights into AmI application design in architecture.