The effect of participation incentives on preference revelations in real discrete choice experiments

Last registered on January 30, 2025
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Pre-Trial

Trial Information

General Information

Title
The effect of participation incentives on preference revelations in real discrete choice experiments
RCT ID
AEARCTR-0014904
Initial registration date
January 28, 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
January 30, 2025, 11:08 AM EST

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Bachir Kassas
Affiliation
University of Florida
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Other Primary Investigator(s)

PI Name
Jerrod Penn
PI Affiliation
Louisiana State University
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PI Name
Wuyang Hu
PI Affiliation
The Ohio State University
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Additional Trial Information

Status
Completed
Start date
2024-12-02
End date
2025-01-06
Keywords
Behavior
Additional Keywords
Discrete Choice Experiments, Participation Incentives, Incentive Compatibility
JEL code(s)
C90, D11
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
To overcome hypothetical bias, many study designs opt for real incentives, introducing consequentiality into the subjects' decisions by linking their participation compensation to decisions and outcomes realized in the study. While this can encourage truthful preference revelations in discrete choice experiments (DCEs), which improves the validity of estimated preference parameters, the common practice for inducing such incentives relies on providing subjects with a participation compensation then deducting any real payments from this compensation based on their decisions and outcomes. This leaves a critical question regarding how the level of participation compensation used impacts subjects' decisions in the study. In other words, does a higher (or lower) participation compensation impact the decisions subjects make in a DCE? This study addresses this question by varying the participation compensation provided to subjects in a real DCE and comparing preference estimates with a hypothetical DCE as a benchmark. Specifically, it looks at two scenarios where the participation compensation is equal to vs. greater than the maximum price level of the product used in the DCE. By comparing behavior in these two scenarios to a hypothetical DCE, this study can provide useful insights on the parameterization of real DCEs to study consumer valuations for different products and product attributes.
External Link(s)

Registration Citation

Citation
Hu, Wuyang, Bachir Kassas and Jerrod Penn. 2025. "The effect of participation incentives on preference revelations in real discrete choice experiments." AEA RCT Registry. January 30. https://doi.org/10.1257/rct.14904-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Each subject will complete 6 choice sets in their DCE, with each choice set containing two alternatives and a no-purchase option. Subjects will be randomly assigned to one of 3 groups as listed below. The products used in the DCE will have a maximum price of $8. The participation compensation is varied in the treatments, where some subjects receive a compensation equal to the maximum price in the DCE (T1) and others receive a compensation greater than the maximum price used in the DCE (T2).

1. Control: subjects in this group will receive $8 in participation compensation, but will complete a hypothetical DCE
2. T1: subjects in this group will receive $8 in participation compensation and will complete a real DCE
3. T2: subjects in this group will receive $12 in participation compensation and will complete a real DCE
Intervention Start Date
2024-12-02
Intervention End Date
2025-01-06

Primary Outcomes

Primary Outcomes (end points)
Subjects' choices in the DCE will be used to estimate their marginal preferences for product attributes. The coefficient estimates on the price attribute and the opt-out option will be compared between the control and two treatments. This will help examine potential differential effects of the two treatments on subjects' price sensitivity and their willingness to opt out of making a purchase.
Primary Outcomes (explanation)
A random parameter logit model will be estimated to obtain preference estimates for the different product attributes. The coefficients on price and opt-out will be interacted with indicator variables for T1 and T2 to examine potential effects of those treatments on subjects' price sensitivity and willingness to opt out of making a purchase in the study.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Each subject will complete 6 choice sets in their DCE, with each choice set containing two alternatives and a no-purchase option. Subjects will be randomly assigned to one of 3 groups. The control group will complete a hypothetical DCE. In the other two treatments, subjects will complete a real DCE, however, their participation compensation will be set to either match the maximum price of the products in the DCE (T1) or exceed this maximum price (T2). A comparison of coefficient estimates on price and opt-out will be conducted between the control and two treatments to determine potential differential effects of the treatments on subjects' price sensitivity and willingness to opt out of making a purchase in the study.
Experimental Design Details
This study will be conducted online using Qualtrics platform to program the experimental instructions. Subjects will answer a few questions about their shopping habits and preferences, complete the DCE based on their treatment, and fill out some sociodemographic information. A representative sample of primary shoppers in the US will be obtained and subjects will be assigned to one of the 3 groups in a manner that ensures balance in subject characteristics across groups. Below is a description of the 3 groups.

1. Control: subjects in this group will receive $8 in participation compensation, but will complete a hypothetical DCE
2. T1: subjects in this group will receive $8 in participation compensation and will complete a real DCE
3. T2: subjects in this group will receive $12 in participation compensation and will complete a real DCE
Randomization Method
Subjects will be randomized across the 3 treatment groups using the randomizer feature in Qualtrics. This feature allows complete randomization of subjects evenly across the treatments to ensure comparable sample sizes and subject characteristics across treatments.
Randomization Unit
Randomization will be conducted at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Each of the three treatment groups will consist of 150 subjects for a total of 450 subjects in the study.
Sample size: planned number of observations
Each subject will make 6 choices in the DCE. With a total of 450 subjects, the total number of observations from the DCE will be 2,700.
Sample size (or number of clusters) by treatment arms
Each of the 3 treatments will consist of 150 subjects.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
LSU Institutional Review Board
IRB Approval Date
2024-10-11
IRB Approval Number
IRBAG-24-0062
Analysis Plan

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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