Self-control and Uninstallment: Evidence on a Large Video-sharing Social Media Platform

Last registered on July 08, 2022
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Pre-Trial

Trial Information

General Information

Title
Self-control and Uninstallment: Evidence on a Large Video-sharing Social Media Platform
RCT ID
AEARCTR-0009143
Initial registration date
July 05, 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
July 08, 2022, 9:12 AM EDT

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

Locations

Country
China
Region

Primary Investigator

Name
Ruru (Juan Ru) Hoong
Affiliation
Harvard University
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Other Primary Investigator(s)

PI Name
Peter Hickman
PI Affiliation
Harvard University
Contact Investigator
PI Name
Yulu Tang
PI Affiliation
Harvard University
Contact Investigator

Additional Trial Information

Status
In development
Start date
2022-07-05
End date
2023-12-31
Keywords
Behavior, Firms & Productivity, Welfare
Additional Keywords
self-control, commitment devices, engagement, social media
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study will conduct a randomised controlled trial (RCT) with 10 million users on a large video-sharing social media platform in China to answer two sets of questions. First, we document the extent of self-control issues (e.g., overuse of the platform, naivete over time usage and uninstallment, what types of content more "addicted" or "naive" people tend to view or be engaged with) on the platform, providing insight to the state of digital addiction for hundreds of millions of people on one of the worlds largest platforms. Second, we investigate if an intervention opting users into a soft commitment device (alarms that pop up after an exogenously varied time limit) impacts time usage patterns on the platform and propensity to uninstall. Based on the heterogeneous treatment effects we also document whether the firm's incentives over who to target for treatment assignment (to reduce uninstallment) coincides with a social planner's optimal targeted treatment assignment (to reduce self-control issues). Our participants will be randomised into 7 groups: a control group, and 6 treatment groups following a factorial design that (i) varies the time at which alarms pop up on their screen (20/60/120 minutes into the user's day) and (ii) whether or not users are shown a "bedtime alarm" or "usage reminder" pop-up screen that tells them to set an alarm that pops up after X mins of use or set a bedtime reminder that pops up at a certain time of night.
External Link(s)

Registration Citation

Citation
Hickman, Peter, Ruru (Juan Ru) Hoong and Yulu Tang. 2022. "Self-control and Uninstallment: Evidence on a Large Video-sharing Social Media Platform." AEA RCT Registry. July 08. https://doi.org/10.1257/rct.9143-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Our intervention comes in the form of a pop-up screen that occurs within the app telling participants either that (i) they've used the app for a long time (or (ii) that it's already late in the evening), and they should either (i) set a reminder to take a break from the app every 30 minutes-8 hours (participants can endogenously set this on a slider scale), or (ii) set a bedtime reminder between 9pm to 1am on a slider scale.

The timing of the pop-up depends on the treatment arm the participant is assigned into. The pop ups will occur either 20, 60, or 120 minutes into the subject's daily use.
Intervention Start Date
2022-07-05
Intervention End Date
2022-08-01

Primary Outcomes

Primary Outcomes (end points)
- Average actual daily usage of platform
- Average stop time (last seen usage) on platform
- Actual - ideal daily usage of platform (proxy for self-control)
- Actual - predicted daily usage of platform (proxy for sophistication)
Primary Outcomes (explanation)
We will also document HTEs. In fact our primary outcome of interest is indeed the HTEs. Note most importantly that we expect that the treatment will be most effective for those with average daily use > 2hours (rather than below), not just because of increased effectiveness of the pop-ups but also because some in the 120 min treatment group (or 60 min) might not actually hit the treatment or be shown the pop-up. When we compare the treatment arms we will look at effects by baseline use so as to have a more apples-to-apples comparison.

We also expect HTEs by baseline difference between actual and ideal, actual and predicted, uninstallment behaviour, engagement metrics, demographics, and so on.

Secondary Outcomes

Secondary Outcomes (end points)
- Uninstallment behaviour
- Engagement on the platform (average percentage of each video watched, % of videos skipped, % of videos liked)
- Opt-out behaviour / adjustment of time limit
- Average "sleep" time (time between last used at night and first used in morning).
Secondary Outcomes (explanation)
We will also document HTEs.

Experimental Design

Experimental Design
The study consists of a randomised controlled trial with 7 arms: a control group, and 6 treatment groups following a factorial design that (i) varies the time at which alarms pop up on their screen (20/60/120 minutes into the user's day) and (ii) whether or not users are shown a "bedtime alarm" or "usage reminder" pop-up screen that tells them to set an alarm that pops up after X mins of use or set a bedtime reminder that pops up at a certain time of night. Our target size for the entire study is ±10 million participants, with 2 million in control, and 1 million in each of the treatment groups.

We will sample from two groups of people: 4 million people who have used the platform between 30 mins and 6 hours daily in the last 2 weeks & are between the age of 18-50, and 6 million people who fulfill the above requirements and moreover have uninstalled (and reinstalled) the app in the last 6 months.

To supplement the administrative data we have from the platform, we will conduct two surveys for a subset of 100k participants: a baseline and a endline survey that pops up in the app. In the week following the baseline survey, participants will be randomised into control and treatment, and treatment group participants will face alarms should they exceed their time limit. The endline survey will be scheduled approximately a month after the start of treatment.
Experimental Design Details
Randomization Method
We will pre-randomise using a random number generator which will be implemented by the platform.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
0 clusters.
Sample size: planned number of observations
10,000,000 users
Sample size (or number of clusters) by treatment arms
2+ million users control, 1+ million users in each treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University Area IRB
IRB Approval Date
2021-11-09
IRB Approval Number
IRB21-1291
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