The Impact of Conspicuous Ad-Transparency on User Engagement

Last registered on April 16, 2024
View Trial History

Pre-Trial

Trial Information

General Information

Title
The Impact of Conspicuous Ad-Transparency on User Engagement
RCT ID
AEARCTR-0013338
Initial registration date
April 10, 2024

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
April 16, 2024, 2:46 PM EDT

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

Locations

Country
India
Region
India

Primary Investigator

Name
Anuj Kapoor
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Joonhyuk Yang
PI Affiliation
Mendoza College of Business, University of Notre Damej
Contact Investigator

Additional Trial Information

Status
In development
Start date
2024-04-13
End date
2024-04-20
Keywords
Behavior, Welfare
Additional Keywords
Advertising, Transparency
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The primary aim of this revised experiment is to delve deeper into the impact of conspicuously revealing information about ad practices to consumers. This is in contrast to a widely adopted inconspicuous ad transparency, where users have to click a button around or in the ad to find out why they see this ad.
External Link(s)

Registration Citation

Citation
Kapoor, Anuj and Joonhyuk Yang. 2024. "The Impact of Conspicuous Ad-Transparency on User Engagement." AEA RCT Registry. April 16. https://doi.org/10.1257/rct.13338-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Step 1: Ad Selection
For external validity, we will continue to use a set of ads exposed to users but
will focus on:
• Inconspicuous ad transparency condition
• Conspicuous ad transparency condition.
The set of ads, denoted by A = {a1, a2, . . . , am}, will be selected to reflect this
focus.
Step 2: Target Population and Experimental Conditions
Define a target population Ua for each ad a ∈ A who will be assigned to:
• Uai: Users exposed to the Inconspicuous ad transparency condition.
• Uaco: Users not exposed to any ad transparency (control group).
These sets must satisfy Ua = Uai∪Uaco and Uai∩Uaco = ∅, with a recommended
split of 50%-50% between the two conditions. Randomization will be conducted
at the user level.
Step 3: Ad Exposure
Users will be shown one of the two versions according to their treatment group
upon any ad impression.
Intervention Start Date
2024-04-14
Intervention End Date
2024-04-20

Primary Outcomes

Primary Outcomes (end points)
Ad response metrics (Click, Conversion, etc.).
Ad Consumption
Ad Complete
• For the Inconspicuous condition, whether the user clicked to see ad transparency information.
• Other outcomes potentially affected by the treatment, such as platform
usage and video completion rates.
Video Consumption
Video Start
Video Q1 Reached
Video Q2 Reached
Video Q3 Reached
Video Q4 Reached
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Step 1: Ad Selection
For external validity, we will continue to use a set of ads exposed to users but
will focus on:
• Inconspicuous ad transparency condition
• Conspicuous ad transparency condition.
The set of ads, denoted by A = {a1, a2, . . . , am}, will be selected to reflect this
focus.
Step 2: Target Population and Experimental Conditions
Define a target population Ua for each ad a ∈ A who will be assigned to:
• Uai: Users exposed to the Inconspicuous ad transparency condition.
• Uaco: Users not exposed to any ad transparency (control group).
These sets must satisfy: Ua = Uai∪Uaco and Uai∩Uaco = ∅, with a recommended
split of 50%-50% between the two conditions. Randomization will be conducted
at the user level.
Step 3: Ad Exposure
Users will be shown one of the two versions according to their treatment group
upon any ad impression.
Experimental Design Details
Step 1: Ad Selection
For external validity, we will continue to use a set of ads exposed to users but
will focus on:
• Inconspicuous ad transparency condition
• Conspicuous ad transparency condition.
The set of ads, denoted by A = {a1, a2, . . . , am}, will be selected to reflect this
focus.
Step 2: Target Population and Experimental Conditions
Define a target population Ua for each ad a ∈ A who will be assigned to:
• Uai: Users exposed to the Inconspicuous ad transparency condition.
• Uaco: Users not exposed to any ad transparency (control group).
These sets must satisfy Ua = Uai∪Uaco and Uai∩Uaco = ∅, with a recommended
split of 50%-50% between the two conditions. Randomization will be conducted
at the user level.
Step 3: Ad Exposure
Users will be shown one of the two versions according to their treatment group
upon any ad impression.
Randomization Method
Coin Flip
Randomization Unit
Individual Level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1.38 Million Ad Impressions
Sample size: planned number of observations
1.38 Million Ad Impressions
Sample size (or number of clusters) by treatment arms
0.69 Million Ad Impressions
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
1.38 Million Ad Impressions based on a power analysis with α = .01 and β = .01.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Indian Institute of Management
IRB Approval Date
2024-02-27
IRB Approval Number
IRB 2023-61
Analysis Plan

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