Individuals' privacy concerns and adoption of contact tracing mobile applications in a pandemic: A situational privacy calculus perspective.

OBJECTIVE: The study sought to develop and empirically validate an integrative situational privacy calculus model for explaining potential users' privacy concerns and intention to install a contact tracing mobile application (CTMA). MATERIALS AND METHODS: A survey instrument was developed based on the extant literature in 2 research streams of technology adoption and privacy calculus. Survey participants (N = 853) were recruited from all 50 U.S. states. Partial least squares structural equation modeling was used to validate and test the model. RESULTS: Individuals' intention to install a CTMA is influenced by their risk beliefs, perceived individual and societal benefits to public health, privacy concerns, privacy protection initiatives (legal and technical protection), and technology features (anonymity and use of less sensitive data). We found only indirect relationships between trust in public health authorities and intention to install CTMA. Sex, education, media exposure, and past invasion of privacy did not have a significant relationship either, but interestingly, older people were slightly more inclined than younger people to install a CTMA. DISCUSSION: Our survey results confirm the initial concerns about the potentially low adoption rates of CTMA. Our model provides public health agencies with a validated list of factors influencing individuals' privacy concerns and beliefs, enabling them to systematically take actions to address these identified issues, and increase CTMA adoption. CONCLUSIONS: Developing CTMAs and increasing their adoption is an ongoing challenge for public health systems and policymakers. This research provides an evidence-based and situation-specific model for a better understanding of this theoretically and pragmatically important phenomenon.

Running field experiments using Facebook split test.

Business researchers use experimental methods extensively due to their high internal validity. However, controlled laboratory and crowdsourcing settings often introduce issues of artificiality, data contamination, and low managerial relevance of the dependent variables. Field experiments can overcome these issues but are traditionally time- and resource-consuming. This primer presents an alternative experimental setting to conduct online field experiments in a time- and cost-effective way. It does so by introducing the Facebook A/B split test functionality, which allows for random assignment of manipulated variables embedded in ecologically-valid stimuli. We compare and contrast this method against laboratory settings and Amazon Mechanical Turk in terms of design flexibility, managerial relevance, data quality control, and sample representativeness. We then provide an empirical demonstration of how to set up, pre-test, run, and analyze FBST experiments.