Privacy Risks and Ambiguity Preferences in the Internet of Things

Last registered on June 06, 2022


Trial Information

General Information

Privacy Risks and Ambiguity Preferences in the Internet of Things
Initial registration date
June 03, 2022

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
June 06, 2022, 5:56 AM 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.
This study investigates whether consumers' preferences between Internet of Things products that are either transparent about the probability of privacy risk or not transparent depend on the magnitude of the communicated risk probability. In an online experiment, we ask subjects to state their preference between an internet-connected security camera for their home that is transparent about the involved privacy risks (i.e., the share of devices of the same brand that have been hacked in the past) and a security camera that is not transparent about these privacy risks (i.e., no information about the probability of past hacks is given). In a between-subjects treatment design, we vary the general risk probability of a hack between ten percent and fifty percent. In particular, this online experiment tests the external validity of findings on subjects' ambiguity preferences in the context of privacy risks from an earlier laboratory experiment (Sachs & Schnurr, 2022,
External Link(s)

Registration Citation

Sachs, Nikolai and Daniel Schnurr. 2022. "Privacy Risks and Ambiguity Preferences in the Internet of Things." AEA RCT Registry. June 06.
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Experimental Details


We run two between-subjects treatments where we vary the general likelihood of a privacy risk that consumers encounter when they buy an internet-connected security camera. We employ one treatment with a ten percent and one treatment with a fifty percent privacy risk. Probabilities are stated to subjects in the description of the decision situation in the online experiment.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Ambiguity preferences of subjects when buying an IoT device which involves privacy risks. This is measured by subjects' decision to purchase either an internet-connected security camera whose manufacturer is transparent about the percentage of hacked cameras of its brand in the past or a security camera whose manufacturer is not transparent about this percentage.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study is run as an online experiment with a scenario-based hypothetical decision environment. Treatments are randomized at the individual subject level. Participants will be recruited from Each subject participates in only one treatment (between-subject design).
Experimental Design Details
Participants of the experiment are recruited from and are blind to the research question of the study. As the experiment is conducted in German, participants must have indicated Germany as their country of residence and be proficient in the German language. The experiment is computerized with the online survey tool LimeSurvey. Participants receive a flat payment of about 3 EUR for their participation in the experiment. Subjects are informed that all information provided by them in the experiment will remain anonymous.

In the experiment, subjects are instructed to imagine they plan to buy an internet-connected smart-security camera for their home. They are told how high experts estimate the percentage of smart security cameras that have been hacked in the past and that they have the choice between two security cameras. Both cameras are equal in terms of price and features but differ with respect to the information their manufacturers give about the percentage of hacked security cameras of that brand in the past, i.e., the probability of privacy risk associated with the purchase of a camera. In the scenario description, subjects are informed that camera A transparently states the percentage of cameras of its brand that have been hacked in the past during their lifetime, while camera B does not state any information on that percentage. In two between-subjects treatments, we vary the general likelihood of a hack between fifty percent and ten percent. See the attached document for a screenshot of the translated description of the decision situation as displayed to subjects in the experiment.

In addition to the two treatments with likelihoods of a hack of ten and fifty percent, a third treatment with a likelihood of one percent may be considered depending on subjects' self reported perceptions of the treatments' risk likelihoods (measured in the experiment on a seven-point Likert scale).

The experiment contains two attention checks. Subjects who fail at least one attention check are automatically screened out of the experiment without finishing it and without being paid.
Randomization Method
Randomization for treatment assignment by using
Randomization Unit
Individual participant
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Observations on the subject level are independent because subjects decide only once without interacting with other subjects 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 1000 subjects.
Sample size (or number of clusters) by treatment arms
500 oberservations per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on a chi-squared test with a statistical power of 0.8 and a significance level of 0.1, with the number of observations, we can detect a minimum effect size of Cohen's d = 0.12
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials