Context Matters: Privacy Perceptions and Data Sharing Behaviors Across Physical and Virtual Environments 8 views

The Internet of Things (IoT) has embedded sensing infrastructure into smart buildings, creating conditions for pervasive data collection that occupants rarely understand or control. While technical safeguards address security, they leave unresolved the human-centered dimensions of privacy: how occupants interpret data practices, assess organizational trust, and decide whether to share personal data. Despite growing interest in privacy-preserving design, there is limited empirical work that examines how smart building occupants reason about sensor data collection, tests whether transparency mechanisms can shift data-sharing decisions, or evaluates these effects under ecologically valid conditions. This dissertation addresses these gaps by (1) establishing how occupants of a smart office building reason about sensor data collection, revealing a stable hierarchy of spatial and data-type sensitivity alongside systematic blind spots about what ambient sensors can infer; (2) isolating elaboration as a specific transparency lever and demonstrating that functional detail about data collection increases perceived essentialness and organizational trust through a serial mediation pathway, with effects concentrated among sensitive data types where functional ambiguity is highest; and (3) introducing virtual reality as a validated research platform for spatially grounded privacy consent research, and showing that transparency effects are deeply conditional on spatial context, with assurance language capable of heightening rather than resolving concern, a pattern termed the bulletproof glass effect. Together, these studies advance a more precise account of when and why transparency interventions work, and offer evidence-based design guidance for user-centered IoT systems.

PHD (Doctor of Philosophy)

Privacy; Transparency; Smart Buildings; Virtual Reality; Decision-making; User Perceptions

English

2026-05-20