The Impact of Algorithmic Recommendation on Time Preference: Evidence from Laboratory and Field Experiments

Last registered on May 06, 2025
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Pre-Trial

Trial Information

General Information

Title
The Impact of Algorithmic Recommendation on Time Preference: Evidence from Laboratory and Field Experiments
RCT ID
AEARCTR-0015949
Initial registration date
May 05, 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
May 06, 2025, 5:30 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
Guangrui Li
Affiliation
York University
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Other Primary Investigator(s)

PI Name
Zheng Gong
PI Affiliation
Chinese University of Hong Kong (Shenzhen)
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PI Name
Xiaoquan(Michael) Zhang
PI Affiliation
Chinese University of Hong Kong
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Additional Trial Information

Status
Completed
Start date
2021-04-28
End date
2023-05-21
Keywords
Behavior, Gender, Lab, Welfare
Additional Keywords
Algorithms
JEL code(s)
D12, D90, L82, L86, I31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Algorithmic recommendation systems on short video platforms significantly impact young adults' time preferences. Our laboratory and field experiments demonstrate that deactivating personalized recommendations increases patience, with pronounced gender differences: male users become substantially more impatient under algorithmic recommendations while female users remain unaffected. This disparity stems from gendered content consumption patterns, particularly males' preference for high-stimulation content. These findings reveal how digital environments can shape fundamental economic preferences, potentially influencing important life outcomes.
External Link(s)

Registration Citation

Citation
Gong, Zheng, Guangrui Li and Xiaoquan(Michael) Zhang. 2025. "The Impact of Algorithmic Recommendation on Time Preference: Evidence from Laboratory and Field Experiments." AEA RCT Registry. May 06. https://doi.org/10.1257/rct.15949-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The participants in the treatment group are asked to turn off the algorithmic recommendation functions on their short video apps.
Intervention (Hidden)
Intervention Start Date
2021-04-28
Intervention End Date
2023-05-21

Primary Outcomes

Primary Outcomes (end points)
Time preference (patience)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Time preference (patience) across gender groups
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Lab Experiment: Lab experiments were conducted at two Chinese universities (S1 and S2), recruiting current students via an online ad offering compensation and a lottery chance. Eligible participants completed a survey reporting their video app usage and availability. From these, users of short video apps with no prior related experiment experience were selected and randomly assigned to treatment or control. Treatment participants were asked to deactivate algorithmic recommendations. All participants completed a baseline survey, browsed short video apps for a fixed time period, and then answered questions on time preferences. S2 participants provided additional data on content types and viewing behavior.

Field Experiment: We conducted four-week field experiments at two Chinese universities (L1 and L2), recruiting participants two weeks prior via a baseline survey. Eligible users who hadn’t joined similar studies were invited to participate. On Day 0, they completed a pre-experiment survey covering screen time, well-being, and time preferences. Participants were randomly assigned to treatment (disabled algorithmic recommendations) or control (default settings), respectively, Weekly and post-experiment surveys tracked usage, activities, and repeated the CTB tasks.
Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
3-4 universities
Sample size: planned number of observations
Lab: around 300 participants; Field: around 200 participants
Sample size (or number of clusters) by treatment arms
In the lab experiment, we planned to have 50% participants into the treatment and 50% into the control. For the field experiment, we planned to have 33% participants into the treatment and 66% into the control, following Allcott et al. (2020).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
OFFICE OF RESEARCH ETHICS OF YORK UNIVERSITY
IRB Approval Date
2021-08-13
IRB Approval Number
e2021-265
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