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Privacy Decision-Making in Digital Markets: Eliciting Individuals’ Preferences for Transparency

Last registered on August 23, 2021


Trial Information

General Information

Privacy Decision-Making in Digital Markets: Eliciting Individuals’ Preferences for Transparency
Initial registration date
August 18, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 23, 2021, 4:32 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator

University of Passau

Other Primary Investigator(s)

PI Affiliation
University of Passau

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Consumers often lack information about how online services collect, use and protect their data. Therefore, transparency is viewed as an essential prerequisite to support consumers in making informed privacy decisions. So far, the literature has primarily studied the consequences of transparency in different data disclosure contexts. However, whether and when individuals actually prefer transparency about privacy risks when given a chance to avoid it remains an open research question. Thus, we investigate individuals’ choices between varying levels of transparency about uncertain losses of personal data. In a randomized controlled online experiment based on a between-subjects Ellsberg-type design, subjects repeatedly choose between a situation of risk, where a loss of personal data will occur with a known probability, and a situation of ambiguity, where a data loss will occur with an unknown probability. By eliciting subjects’ revealed preferences in a controlled environment, we provide novel insights into why and when individuals may avoid transparency about privacy risks. In particular, we investigate whether subjects exhibit ambiguity aversion as found for uncertain monetary losses by previous studies. Moreover, we vary the general probability of a data loss in the experiment to analyze whether transparency preferences are contingent on the loss probability. Altogether, these insights contribute to a better understanding of whether individuals actually make use of transparency about privacy risks and thus shed light on firms’ incentives to be transparent about their data use and the associated risks.
External Link(s)

Registration Citation

Sachs, Nikolai and Daniel Schnurr. 2021. "Privacy Decision-Making in Digital Markets: Eliciting Individuals’ Preferences for Transparency." AEA RCT Registry. August 23.
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Experimental Details


We run two treatments, where we compare a high general probability of data loss against a low probability of data loss. Within a treatment, we analyze subjects’ ambiguity preferences regarding a data loss. We also consider an additional optional treatment, where the general setting is compared to a framed setting.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Ambiguity preferences of subjects for a data loss (ambiguity aversion or ambiguity seeking)
Primary Outcomes (explanation)
Ambiguity preferences are measured by probability equivalents of subjects' choices in an Ellsberg-type experiment and their deviations from the ambiguity-neutral probability.

Secondary Outcomes

Secondary Outcomes (end points)
Change of ambiguity preferences for a change in the ambiguity-neutral probability of a data loss.
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
Not available
Randomization Method
Randomization by computer in office
Randomization Unit
Experimental sessions
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Observations on the subject level are assumed to be independent, because subjects decide only once without interacting with other participants in the experimental session before their decision. 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. This corresponds to six experimental sessions each, with 15 participants in a single session.
Sample size (or number of clusters) by treatment arms
90 oberservations per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For our primary and secondary outcome we aim to detect an effect size of at least d = 0.4.

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
IRB Approval Number
IRB Name
University of Passau Research Ethics Committee
IRB Approval Date
IRB Approval Number