Predicted Adoption Rates of Contact Tracing App Configurations - Insights from a choice-based conjoint study with a representative sample of the UK population
Wiertz, C., Banerjee, A. 
ORCID: 0000-0001-8961-7223, Acar, O. A. ORCID: 0000-0003-1993-0921 & Ghosh, A. (2020).
Predicted Adoption Rates of Contact Tracing App Configurations - Insights from a choice-based conjoint study with a representative sample of the UK population.
London, UK: Cass Business School, City, University of London.
Abstract
Widespread adoption of a contact tracing app by the UK public is an important part of safely easing or lifting the lockdown. In this context, it is essential to understand how adoption rates are influenced by different configurations of a proposed contact tracing app. There are many implementation options that can impact app adoption. For example, which institution should be responsible for and have oversight of the app? What type of data is collected? Does it matter how long it is stored? This whitepaper provides data-driven insights into these and other questions to guide app implementation choices.
| Publication Type: | Report |
|---|---|
| Additional Information: | copyright, the authors, 2020. |
| Publisher Keywords: | COVID-19; coronavirus; contract tracing; contact tracing app; app adoption; privacy |
| Subjects: | G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography H Social Sciences > HM Sociology Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
| Departments: | Bayes Business School > Management |
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