Avoiding Negative Effects of Negative Information

Last registered on August 29, 2019
View Trial History

Pre-Trial

Trial Information

General Information

Title
Avoiding Negative Effects of Negative Information
RCT ID
AEARCTR-0004609
Initial registration date
August 26, 2019

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 29, 2019, 8:43 AM EDT

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

Locations

Country
Australia
Region
Country
Canada
Region
Country
United Kingdom of Great Britain and Northern Ireland
Region
Country
New Zealand
Region
Country
United States of America
Region

Primary Investigator

Name
Solene Delecourt
Affiliation
UC Berkeley - Haas School of Business
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Jessica Fong
PI Affiliation
University of Michigan
Contact Investigator

Additional Trial Information

Status
In development
Start date
2019-09-01
End date
2022-02-15
Keywords
Firms & Productivity, Health
Additional Keywords
Marketing, Information Avoidance, Negative Information
JEL code(s)
Secondary IDs
Abstract
More information should always be better, but it can have negative consequences, especially when the information is negative. For example, bad news can be demotivating. Relatedly, people may avoid freely available information, especially if it contains bad news. In this paper, we ask: How can we mitigate the negative effects of bad news? We test ways to mitigate potential negative effects of negative information using a randomized field experiment. We collaborate with a large company selling wearable activity trackers that provide users with personal information on the duration and quality of their sleep. We conduct an experiment with 1 million users for 4 weeks that randomizes the content of messages that goes along with information when the quality of their sleep has dropped. The types of messaging we test not only measures the most effective content at preventing negative consequences of bad news, but also information avoidance and boosting effort/motivation. They also provide insight into the mechanisms of how types of information can affect behavior. Therefore, we can apply these types of messaging to improve the deliverance of other information in general. It has clear implications on how to help firms and other organizations deliver crucial negative information to their users in a more effective manner.
External Link(s)

Registration Citation

Citation
Delecourt, Solene and Jessica Fong. 2019. "Avoiding Negative Effects of Negative Information ." AEA RCT Registry. August 29. https://doi.org/10.1257/rct.4609-1.0
Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
Experimental Details

Interventions

Intervention(s)
We conduct an experiment with 1 million users for 4 weeks that randomizes the content of messages that goes along with information when the quality of their sleep has dropped.
Intervention Start Date
2019-09-30
Intervention End Date
2020-01-15

Primary Outcomes

Primary Outcomes (end points)
Effect on engagement: are users looking at sleep data the next day?
Did the user wear their device to bed the next day?

Effect on motivation:
Are users following (reinforcing) good sleep habits? (bedtime, sleep duration, wake up time)
Effects on other health behaviors the next day: exercise (steps, active min)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We plan to look at heterogeneity of the treatment effect by demographics, gender, age, baseline activity level (step count and active minutes), baseline sleep quality (prior to the experiment).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conduct an experiment with 1 million users for 4 weeks that randomizes the content of messages that goes along with information when the quality of their sleep has dropped.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
NA
Sample size: planned number of observations
1 million active users.
Sample size (or number of clusters) by treatment arms
We have 1 control group and 5 treatment groups, so that is about ~165,000 individuals per group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

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