Information Campaigns for a Healthy Online Ad Ecosystem

Last registered on February 20, 2025
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Pre-Trial

Trial Information

General Information

Title
Information Campaigns for a Healthy Online Ad Ecosystem
RCT ID
AEARCTR-0015051
Initial registration date
February 16, 2025

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
February 20, 2025, 5:14 AM EST

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

Locations

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Primary Investigator

Name
Ananya Sen
Affiliation
Carnegie Mellon University
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Other Primary Investigator(s)

PI Name
Xilan Zhang
PI Affiliation
Stanford University
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PI Name
Katherine DeCelles
PI Affiliation
University of Toronto
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PI Name
Charles Eesley
PI Affiliation
Stanford University
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Additional Trial Information

Status
In development
Start date
2025-02-12
End date
2026-02-01
Keywords
Behavior
Additional Keywords
Information Intervention, Field Experiment, Misinformation, Online Ads
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The purpose of this study is to understand which types of informational messages are most effective in encouraging website visitors to engage with a stakeholder group, which works to create a healthier digital advertising ecosystem. Specifically, the study tests how different messages highlighting factors like community size, protection of public interest, and support by institutional entities impact visitor behavior, such as signing up for newsletters or exploring the website further. By analyzing these responses, the study aims to identify the most persuasive strategies for mobilizing support and building community engagement. This research will help organizations better communicate their mission and connect with the public.
External Link(s)

Registration Citation

Citation
DeCelles, Katherine et al. 2025. "Information Campaigns for a Healthy Online Ad Ecosystem." AEA RCT Registry. February 20. https://doi.org/10.1257/rct.15051-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This study involves displaying randomized pop-up messages to visitors of a stakeholder group’s website. The messages highlight factors such as community size, protection of public interests, and endorsements from well-known institutions. The aim is to explore how these elements influence visitor behavior, including signing up for newsletters or engaging further with the website.
Intervention Start Date
2025-02-12
Intervention End Date
2026-02-01

Primary Outcomes

Primary Outcomes (end points)
Newsletter signup
Primary Outcomes (explanation)
The key outcome that our partner cares about is growing their newsletter audience.

Secondary Outcomes

Secondary Outcomes (end points)
Browsing behavior and return visits to the website
Secondary Outcomes (explanation)
Secondary outcomes focus on evaluating visitor engagement through metrics such as browsing behavior and return visits.

Experimental Design

Experimental Design
Participants in this study are visitors to a stakeholder group’s website. Upon arriving at the homepage, visitors will be randomly assigned to one of four groups: three treatment groups and one control group. Each group will see a different pop-up message highlighting community size, protection of public interests, or endorsements from well-known institutions; the control group will receive a neutral message.
Experimental Design Details
Not available
Randomization Method
Each individual will be randomized into one of the three treatment groups or the control group using computer software.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1 website (the stakeholder group’s homepage) will serve as the cluster for the intervention.
Sample size: planned number of observations
Since such an experiment cannot have pre-testing, it is unclear how many observations we will need to detect effects. We aim for at least 1,000 website visitors per arm. Each visitor represents one observation. The number of observation may vary on the website traffic.
Sample size (or number of clusters) by treatment arms
The sample will be divided equally across the four groups.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Stanford University
IRB Approval Date
2025-02-07
IRB Approval Number
FWA00000935
Analysis Plan