COVID-19 Apps and Privacy Protections from Users' Perspective.

As the spread of the novel coronavirus (COVID-19) continues to be a global challenge, there have been numerous efforts and actions from both government and private organizations towards keeping their community members healthy and safe. One of the approaches is to use mobile apps to trace contacts and update the status of the infected individuals efficiently and conveniently so that the spread of COVID-19 can be minimized and contained. While these apps could offer many advantages, it also raises serious privacy concerns for many users and hence possibly refusing to adopt it. In this study, we aim to understand the users' expectations on the privacy protections and the provisions under which they are willing to use COVID-19 apps. We believe our study results can guide policymakers and app developers on the design, deployment, and acceptability of the COVID-19 apps that can be widely adopted.

Users' Adoption of Mental Health Apps: Examining the Impact of Information Cues.

BACKGROUND: Numerous mental health apps have been developed and made available to users on the current app market. Users may find it difficult and overwhelming to select apps from the hundreds of choices that are available in the app marketplace. Clarifying what information cues may impact a user's selection and adoption of mental health apps is now a critical and pressing issue. OBJECTIVE: The aim of this study was to investigate the impact of information cues on users' adoption of anxiety apps using observational data from the Android app market. METHODS: A systematic search of anxiety apps was conducted on the Android app store by using keywords search. The title and metadata information of a total of 274 apps that met our criteria were collected and analyzed. Three trained researchers recorded the app rankings from the search results page on different dates and Web browsers. RESULTS: Our results show that ratings (r=.56, P<.001) and reviews (r=.39, P<.001) have significant positive correlations with the number of installs, and app prices have significant negative correlations with installs (r=-.36). The results also reveal that lower-priced apps have higher ratings (r=-.23, P<.001) and a greater number of app permission requests (r=.18, P=.002) from the device. For app titles, we found that apps with titles related to symptoms have significantly lower installs than apps with titles that are not related to symptoms (P<.001). CONCLUSIONS: This study revealed a relationship between information cues and users' adoption of mental health apps by analyzing observational data. As the first of its kind, we found impactful indicators for mental health app adoptions. We also discovered a labeling effect of app titles that could hinder mental health app adoptions and which may provide insight for future designs of mental health apps and their search mechanisms.

Trust in automation: integrating empirical evidence on factors that influence trust.

OBJECTIVE: We systematically review recent empirical research on factors that influence trust in automation to present a three-layered trust model that synthesizes existing knowledge. BACKGROUND: Much of the existing research on factors that guide human-automation interaction is centered around trust, a variable that often determines the willingness of human operators to rely on automation. Studies have utilized a variety of different automated systems in diverse experimental paradigms to identify factors that impact operators' trust. METHOD: We performed a systematic review of empirical research on trust in automation from January 2002 to June 2013. Papers were deemed eligible only if they reported the results of a human-subjects experiment in which humans interacted with an automated system in order to achieve a goal. Additionally, a relationship between trust (or a trust-related behavior) and another variable had to be measured. All together, 101 total papers, containing 127 eligible studies, were included in the review. RESULTS: Our analysis revealed three layers of variability in human-automation trust (dispositional trust, situational trust, and learned trust), which we organize into a model. We propose design recommendations for creating trustworthy automation and identify environmental conditions that can affect the strength of the relationship between trust and reliance. Future research directions are also discussed for each layer of trust. CONCLUSION: Our three-layered trust model provides a new lens for conceptualizing the variability of trust in automation. Its structure can be applied to help guide future research and develop training interventions and design procedures that encourage appropriate trust.