Making the (Social) Impact of Information Avoidance Transparent: Evidence from an Online Experiment

Last registered on March 12, 2026

Pre-Trial

Trial Information

General Information

Title
Making the (Social) Impact of Information Avoidance Transparent: Evidence from an Online Experiment
RCT ID
AEARCTR-0017290
Initial registration date
March 09, 2026

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
March 12, 2026, 4:24 AM EDT

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

Locations

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Primary Investigator

Affiliation
Ulm University

Other Primary Investigator(s)

PI Affiliation
Ulm University
PI Affiliation
Ulm University

Additional Trial Information

Status
In development
Start date
2026-03-12
End date
2026-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Organizations depend on employees to proactively seek and use decision-relevant information, yet individuals frequently engage in information avoidance, deliberately staying uninformed even when additional information would improve decision quality. Such avoidance behavior can impair team effectiveness and organizational performance, particularly when employees overlook information with meaningful consequences for others. Although prior research has identified drivers of this phenomenon, evidence on effective interventions to reduce avoidance remains limited.
We theorize that making the impact of avoidance transparent reduces individuals’ propensity to remain uninformed by increasing the salience and perceived relevance of information acquisition, thereby limiting self-serving justifications for ignorance.
To examine this proposition, we conduct an online experiment testing whether impact transparency decreases information avoidance in organizational decision-making contexts.
External Link(s)

Registration Citation

Citation
Hirt, Simon, Natalie Rupp and Mischa Seiter. 2026. "Making the (Social) Impact of Information Avoidance Transparent: Evidence from an Online Experiment." AEA RCT Registry. March 12. https://doi.org/10.1257/rct.17290-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-03-12
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
Information Avoidance; Information Use
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In this study, participants complete two decision-making tasks designed to capture their willingness to acquire and use information:

1. Supplier Selection Task: Participants choose between suppliers. Then, they may opt to access additional information about the suppliers and change their initial decision.
2. Order Quantity Estimation Task: Participants estimate quantities to order. They may choose to receive feedback regarding their performance and change their initial decision.

To control for potential order effects, the sequence of these two tasks is randomized across participants. Participants are randomly assigned to one of three experimental groups:

• Control Group: No informational cue is displayed; participants make decisions without any explicit reference to the consequences of information avoidance.
• Treatment Groups: Participants receive an informational cue highlighting the external consequences associated with avoiding information on i) their coworkers or ii) their company. The cue is designed to make the impact of avoidance salient.
Experimental Design Details
Not available
Randomization Method
Participants will be randomly assigned to the experimental groups by a designated function of the software SoSci Survey.
Randomization Unit
Randomization will be done at the participant level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
Our sample size will be calculated based on the results of our experimental pretest using G-Power Analysis.
Sample size (or number of clusters) by treatment arms
Our sample size will be calculated based on the results of our experimental pretest using G-Power Analysis.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board of the Faculty of Mathematics and Economics (IRB-MAWI), University of Ulm
IRB Approval Date
2026-03-06
IRB Approval Number
Project 2026-01