Data Donations for Digital Contact Tracing: Short- and Long-Term Effects of Monetary Incentives

Abstract

Data donations promise to unlock the social benefits of personal data. Recently, contact-tracing apps were developed to collect contact and health data from individuals to fight the COVID-19 pandemic. Compared to commercial apps, the adoption of contact-tracing apps involves a unique cost-benefit calculus. The pro-social motives to engage in data donations, a mix of short- and long-term costs, and the need for continuous, yet mostly passive app usage render digital contact tracing a novel IS adoption setting. Because the effectiveness of contact-tracing apps hinges on widespread adoption and continuous data collection, we use a randomized controlled online experiment to evaluate the effectiveness of different monetary incentive mechanisms at promoting verified installations of the German Corona-Warn-App and short- and long-term data donations. We find that monetary incentives are effective in the short term, with no evidence of a crowding-out of pro-social motivations: Monetary incentives significantly increase app installations and short-term data donations, tripling the number of data donors after 14 days compared to a no-compensation treatment. However, the positive stimulus of monetary incentives vanishes in the long term: After eight months, installers in treatments with monetary incentives are significantly more likely to have stopped donating data than intrinsically motivated installers who did not receive monetary incentives, as a consequence of experienced opportunity costs and a lack of perceived benefits. Consequently, long-term data donation rates are not significantly higher in treatments with monetary incentives. This suggests that one-time payments are ineffective at promoting long-term data donations, as the short-term crowding-in of less intrinsically motivated installers is difficult to sustain when passive app usage limits opportunities for habit formation and convincing users of contact-tracing benefits. Finally, we present experimental evidence that empirical analyses based on hypothetical scenarios without verified actions are prone to overestimating individuals' pro-social behavior in data donation contexts.

Keywords: data donation, data altruism, contact-tracing apps, app adoption, incentives, pro-social behavior, behavioral economics, COVID-19, experiment

JEL Classification: I12, H41, D64