Privacy Suspension with Sustainability and Trust in Consumer Adoption of Smart Technology

Smart technology, such as the internet of things, artificial intelligence, and big data, provides consumers with a new level of convenience through various smart-connected products (SCPs). Although many experts have increasingly warned about the privacy vulnerability issues of various SCPs, consumers often underestimate privacy risks when adopting smart technology. Accordingly, this dissertation presents a literature review and three empirical studies that examine the privacy problems and suggest new concepts and models for a deeper understanding of the privacy suspension phenomenon. The first chapter reviews the literature related to the privacy suspension phenomenon by integrating the antecedents of consumers' privacy concerns. New concepts of privacy concerns, such as active and inactive privacy concerns, are suggested along with multiple propositions for the proposed privacy suspension theory, which extends the dimension of ambivalence toward trust and distrust regarding smart technology. The second chapter presents the proposed privacy–common good trade-off model and three assumptions related to privacy trade- offs, privacy reduction, and anchoring effects in the sustainable smart-connected car context. This study also discusses the relationships between governments, companies, and consumers regarding the effects of the common good of sustainability and government subsidies. The third chapter evaluates the mediation effects between sustainability, trust, privacy concerns, disclosure intentions, and purchase intentions when purchasing sustainable smart-connected cars based on the proposed sustainability–trust–behavior model. Finally, the fourth chapter provides a practical solution to resolve privacy suspension issues using the design science research approach. This study proposes privacy information type characteristics to evaluate SCPs' tailored data collection capabilities, visualizing them through a spider diagram design method with nudges.