Incentivizing Data Donations: Can Monetary Compensation Increase Data Contributions?

Last registered on July 13, 2020

Pre-Trial

Trial Information

General Information

Title
Incentivizing Data Donations: Can Monetary Compensation Increase Data Contributions?
RCT ID
AEARCTR-0006148
Initial registration date
July 13, 2020
Last updated
July 13, 2020, 2:36 PM EDT

Locations

Region

Primary Investigator

Affiliation
University of Passau

Other Primary Investigator(s)

PI Affiliation
University of Passau

Additional Trial Information

Status
In development
Start date
2020-07-15
End date
2021-03-31
Secondary IDs
Abstract
Today, individuals commonly disclose personal data to enjoy the benefits of data-driven services, such as personalized user interfaces and targeted content recommendations. Next to these personal benefits, data from individuals can also create great societal returns in the public interest. In this spirit, several countries have introduced mobile tracking apps in response to the COVID 19-pandemic, to facilitate contact tracing based on the continuous collection of users’ contact data with others. However, such tracking apps can only represent effective building blocks for a nation’s public health strategy if individuals are willing to voluntarily donate their data by installing and using these apps. Despite the potential societal benefits, empirical research on data donations is still scarce. In particular, it is unknown which mechanism can be effective in encouraging individuals to donate their data in the public interest and whether monetary payments can increase the willingness to contribute data. Previous studies on other types of donations, such as blood donations, show that monetary compensation can crowd out intrinsic motivation and altruistic motives, and thus, reduce the number of blood donors. However, in the context of data, monetary compensation could provide a short-term stimulus that may foster long-term data donations. To address this empirical research question, we run an experimental study and compare the effect of different incentives on participants’ willingness to donate data. Altogether, our findings provide timely evidence on how to encourage data donations in the interest of public health.
External Link(s)

Registration Citation

Citation
Fast, Victoria and Daniel Schnurr. 2020. "Incentivizing Data Donations: Can Monetary Compensation Increase Data Contributions?." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.6148-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We conduct an online experiment and elicit subjects’ (revealed) willingness to install and use the Corona-Warn-App that is provided by the German federal government. We vary the type of incentive offered to the subjects for the installation and usage of the app.
Intervention Start Date
2020-07-15
Intervention End Date
2020-12-31

Primary Outcomes

Primary Outcomes (end points)
Subjects’ decision to install the Corona-Warn-App on their own smartphone (verified by experimenter).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Subjects’ revealed use of the Corona-Warn-App (verified 14 days after installation).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Experiments are run online. Treatments are randomized at the session level. Participants will be recruited from the student subject pool of the University of Passau. Each subject participates in only one treatment (between-subject design). In all treatments, subjects are fully informed about the timeline of the experiment and the consequences of their actions.
Experimental Design Details
All subjects receive a participation fee. Depending on the treatment, subjects receive an additional compensation. Subjects also receive a participation fee if they participate in a follow-up-survey where app usage is verified. We record subjects' willingness to install and use the Corona-Warn-App as well as subjects' answers to a questionnaire regarding privacy attitudes, technology acceptance, personality characteristics, health status and demographics.

Details:
Participants will be recruited from the subject pool of the University of Passau using ORSEE (Greiner, 2015) and they are paid a participation fee of 12 Euro each. As the experiment is conducted in German, participants must be proficient in the German language. A session is expected to last about 60 minutes. The online experiment is programmed using the survey software LimeSurvey. Participants are fully informed about the timeline of the experiment and are informed about the pseudonymous collection of subject data in the experiment, to which they must agree in order to participate. Each participant may exit the experiment at any time.

The general experimental procedures are as follows: After the instructions are read aloud via a video conference tool (in which subjects remain anonymous), participants have to answer several comprehension questions. Next, the purpose and goals as well as the technical details and the data collection of the Corona-Warn-App are presented to the participants. Then, participants have to decide whether they want or do not want to install (and use) the Corona-Warn-App. They can also state that they are already using the app and want to continue to use it. Afterwards, participants have to fill out a questionnaire on their privacy attitudes, technology acceptance, personality characteristics, health status and demographics. For participants who do not want to install the Corona-Warn-App, the experiment ends after completion of the questionnaire. Participants who decide to install the Corona-Warn-App or state that they are already using the app, have to verify the installation of the app by showing the active app on their smartphone, together with their participation ID, to the experimental instructor via their webcam. Subjects that opted for installation or continued use of the app, are sent a follow-up-questionnaire after 14 days. As part of this questionnaire, participants have to verify their usage of the app by uploading a photo of the app’s usage tracker (‘Risk tracking is active for 14 days’) or indicate that they have stopped using the app.
Randomization Method
Randomization by computer in office
Randomization Unit
Experimental sessions
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Observations on the subject level are assumed to be independent, because subjects decide only once on app installation and decide without interacting with other participants in the experimental session. Thus, the number of clusters equals the number of observations.
Sample size: planned number of observations
We schedule data collection aiming at 90 observations per treatment. Thus, we aim for a total of 360 individual participants across the four treatments.
Sample size (or number of clusters) by treatment arms
90 (student) participants per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2020-07-13
IRB Approval Number
BvBHiLIW

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials