Incentivising the Adoption of COVID-19 Contact-Tracing Apps: A Randomised Controlled Online Experiment on the German Corona-Warn-App

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Abstract

Contact-tracing apps constitute a key pillar of many nations' public health strategy to contain the COVID-19 pandemic. However, the effectiveness of digital contact-tracing depends crucially on widespread app installation and usage among citizens. Despite the large social benefits, evidence on the effectiveness of incentives to stimulate adoption of contact-tracing apps is scarce. Whereas monetary compensations for app installation or usage provide users with a direct economic benefit, research in related health domains has found that extrinsic incentives may crowd out intrinsic motivation and pro-social behaviour. Thus, we conduct a randomised controlled online experiment to evaluate the effectiveness of different incentive mechanisms in promoting the installation and usage of the German Corona-Warn-App (CWA). We find that monetary incentives are effective in significantly increasing verified app installations and app usage 14 days after installation, up to three times the usage without compensation. Deferred monetary compensation for verified usage yields lower installation rates than immediate monetary compensation for installation, but sustains the highest retention rate among adopters. Non-monetary compensation in the form of optional charity donations is found to be ineffective in promoting installations or usage beyond outcomes of monetary incentives, suggesting that there is no substantial crowding-out of intrinsic motives. This suggests that monetary incentives can serve as an important policy instrument to encourage the adoption of contact-tracing apps and contain the spread of COVID-19.

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APA

Fast, V., & Schnurr, D. (2021). Incentivising the Adoption of COVID-19 Contact-Tracing Apps: A Randomised Controlled Online Experiment on the German Corona-Warn-App. In SIGMIS-CPR 2021 - Proceedings of the 2021 Computers and People Research Conference (pp. 31–33). Association for Computing Machinery, Inc. https://doi.org/10.1145/3458026.3462158

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