Fast, Victoria and Schnurr, Daniel (2026) Data Donations for Digital Contact Tracing: Short- and Long-Term Effects of Monetary Incentives. INFORMATION SYSTEMS RESEARCH, 37 (1). pp. 627-642. ISSN 1047-7047, 1526-5536
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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 with commercial apps, the adoption of contacttracing apps involves a unique cost-benefit calculus. The prosocial 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 information systems 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 prosocial motivations: Monetary incentives significantly increase app installations and short-term data donations, tripling the number of data donors after 14 days compared with 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' prosocial behavior in data donation contexts.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INFORMATION-SYSTEMS; STATUS-QUO; PRIVACY; REWARDS; HABIT; data donation; data altruism; contact-tracing apps; app adoption; incentives; prosocial behavior; experiment; behavioral economics; COVID-19 |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Information Systems > Chair of Machine Learning and Uncertainty Quantification (Prof. Dr. Daniel Schnurr) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 17 Jun 2026 04:19 |
| Last Modified: | 17 Jun 2026 04:19 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66344 |
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