Healthcare Informatics & Health Information Technology (SIG Health)
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Paper Type
Complete
Paper Number
1146
Description
Contact-tracing apps are considered one of the core information technologies to help contain the spread of the COVID-19 pandemic. However, they need to be adopted broadly to be effective. In this study, we apply the concept of algorithmic transparency (AT) to contact-tracing apps and hypothesize that users prefer apps with a high level of disclosure of information about the app's inner workings (referred to as transformation AT) and that disclosure increases user comprehension and trust. We empirically validate our hypotheses through an online experiment with 116 participants. We find that the level of transformation AT of a contact-tracing app is positively related to users' adoption behavior, comprehension, and trust.
Recommended Citation
Bitzer, Tobias; Wiener, Martin; and Morana, Stefan, "Algorithmic Transparency and Contact-tracing Apps – An Empirical Investigation" (2021). AMCIS 2021 Proceedings. 4.
https://aisel.aisnet.org/amcis2021/healthcare_it/sig_health/4
Algorithmic Transparency and Contact-tracing Apps – An Empirical Investigation
Contact-tracing apps are considered one of the core information technologies to help contain the spread of the COVID-19 pandemic. However, they need to be adopted broadly to be effective. In this study, we apply the concept of algorithmic transparency (AT) to contact-tracing apps and hypothesize that users prefer apps with a high level of disclosure of information about the app's inner workings (referred to as transformation AT) and that disclosure increases user comprehension and trust. We empirically validate our hypotheses through an online experiment with 116 participants. We find that the level of transformation AT of a contact-tracing app is positively related to users' adoption behavior, comprehension, and trust.
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