Privacy Management and Health Information Sharing via Contact Tracing during the COVID-19 Pandemic: A Hypothetical Study on AI-Based Technologies.

ABSTRACT

In this study, we extended and tested the privacy calculus framework in the context of a hypothetical AI-based contact-tracing technology for application during the COVID-19 pandemic that is based on the communication privacy management and contextual integrity theories. Specifically, we investigated how the perceived privacy risks and benefits of information disclosure affect the public's willingness to opt in and adopt contact-tracing technologies and how social and contextual factors influence their decision-making process. Four hundred eighteen adults in the United States participated in the study via Amazon Mechanical Turk in August 2020. A percentile bootstrap method with 5,000 resamples and bias-corrected 95% confidence intervals in structural equation modeling was used for data analysis. The participants' privacy concerns and perceived benefits significantly influenced their opt-in and adoption intentions, which suggests that the privacy calculus framework applies to the context of COVID-19 contact-tracing technologies. Perceived social, personal, and reciprocal benefits were identified as crucial mediators that link contextual variables to both opt-in and adoption intentions. Although this study was based on a hypothetical AI-based contact-tracing app, our findings provide meaningful theoretical and practical implications for future research investigating the public's technology adoption in contexts where tradeoffs between privacy risks and public health coexist.