Designing plant-based defaults to maximize uptake while minimizing reactance

Last registered on July 06, 2026

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Pre-Trial

Trial Information

General Information

Title

Designing plant-based defaults to maximize uptake while minimizing reactance

RCT ID

AEARCTR-0019102

Initial registration date

July 03, 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

July 06, 2026, 9:31 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

Name

Seth Green

Affiliation

Stanford University

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Other Primary Investigator(s)

PI Name

Maya Mathur

PI Affiliation

Stanford University

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PI Name

Kenjin Chang

PI Affiliation

Greener By Default

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PI Name

Natalia Lawrence

PI Affiliation

University of Exeter

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Additional Trial Information

Status

In development

Start date

2026-07-13

End date

2026-08-24

Keywords

Behavior

Additional Keywords

nudge, behavioral science, plant-based defaults, defaults

JEL code(s)

C91, D91, D12, Q18

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Plant-based defaults are widely touted as a scalable, sustainable solution for increasing uptake of plant-based foods. However, defaults are not a singular thing; their designs can be varied to make opting out more or less difficult, or they can be accompanied by persuasive text that promotes the change to choice architecture to participants' conscious awareness. This experiment -- a collaboration between researchers at Stanford, the University of Exeter, and the nonprofit Greener By Default -- compares four plant-based defaults in an online context on two dimensions: how much they increase uptake of plant-based food and how much reactance they engender (re: how much they annoy people). We will test four plant-based defaults in the setting of a hypothetical catered lunch to see which performs best on these two axes: a standard default where the plant-based option is pre-selected and a participant can switch to the meat option, a nonstandard default where the plant-based option is pre-selected and a user must unclick it (and then select nothing) to get a meat-based lunch, a standard plant-based default along with a message about inclusivity, and a standard plant-based default along with a message about sustainability. We will measure reactance by asking how satisfied participants were with their meal order. We will also conduct some exploratory analyses based on demographic information collected.

External Link(s)

Registration Citation

Citation

Chang, Kenjin et al. 2026. "Designing plant-based defaults to maximize uptake while minimizing reactance." AEA RCT Registry. July 06. https://doi.org/10.1257/rct.19102-1.0

Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)

Participants imagine attending an event with a free catered lunch and choose between two meals: a Tofu Bánh Mì (plant-based) and a Chicken Bánh Mì. We randomize the format in which this choice is presented. In the control condition, neither option is pre-selected. Four treatments make the plant-based meal the default: T1 pre-selects it on a standard two-option ballot; T2 presents a single pre-checked checkbox that participants must uncheck to receive the chicken option instead; T3 and T4 use the T1 ballot plus a short justification, framing the plant-based meal as the most inclusive option (T3) or the most sustainable option (T4).

Intervention Start Date

2026-07-13

Intervention End Date

2026-08-24

Primary Outcomes

Primary Outcomes (end points)

(1) Choice of the plant-based meal (chose_veg). (2) Dissatisfaction with the chosen meal (dissat_chosen).

Primary Outcomes (explanation)

chose_veg = 1 if the participant ends up with the Tofu Bánh Mì and 0 if the
Chicken Bánh Mì. In the arms with a two-option ballot (control, T1, T3, T4),
this is the option selected. In T2 (opt-out checkbox), leaving the pre-checked
box checked codes as 1 and unchecking it codes as 0; a blank checkbox field
is a deliberate opt-out, not a missing value. dissat_chosen = 1 if the
participant rates satisfaction with the meal they chose (1 = extremely
dissatisfied to 5 = extremely satisfied) in the bottom two boxes (<= 2).
Confirmatory criteria: H1 holds for an arm if its estimated uptake gain over
control is significant at p < .01 (linear probability model, HC2 robust
standard errors) and meets a pre-registered minimum of 10 percentage points
(T1), 20 (T2), or 30 (T3, T4). H2 holds for an arm if its dissatisfaction rate
and the upper bound of the rate's 95% Wilson confidence interval both fall
below a pre-registered ceiling of 20% (T1, T3, T4) or 30% (T2). Control has no
registered ceiling; we report its rate against a 10% descriptive benchmark.

Secondary Outcomes

Secondary Outcomes (end points)

Dissatisfaction with the available meal choices; dissatisfaction with how the
choices were presented; manipulation check (reported meal matches recorded
choice); perceived descriptive norms (estimated percentage of participants
choosing the same meal). Pre-specified exploratory analyses: pooled framed
defaults (T3 + T4) vs. the bare default (T1) on both primary outcomes;
cross-condition comparisons among treatment arms; dietary identity coded from
open text; heterogeneous treatment effects by gender, education, and age.

Secondary Outcomes (explanation)

The two secondary satisfaction items use the same 1-5 scale and bottom-two-box
dissatisfaction coding as the primary reactance outcome; both are reported
descriptively with 95% Wilson confidence intervals and carry no registered
ceilings. The manipulation check is the share of participants whose reported
meal matches their recorded choice. Norms are measured on a 0-100 slider.
Dietary identity is coded from an optional free-text item, with coding
finalized after inspecting real responses. Exploratory analyses carry no
confirmatory status.

Experimental Design

Five-arm between-subjects survey experiment on Prolific (US adults, N =
3,750). Participants imagine a catered event and choose between a plant-based
and a chicken sandwich under one of five randomly assigned choice formats: no
default, plant-based pre-selected, plant-based opt-out checkbox, pre-selected
plus an inclusivity message, or pre-selected plus an environmental message.
Each format is evaluated on two co-primary outcomes: plant-based uptake,
tested against a pre-registered minimum gain over control, and dissatisfaction
with the chosen meal, tested against a pre-registered acceptability ceiling.
Analyses are linear probability models with HC2 robust standard errors
(unadjusted primary, covariate-adjusted robustness) and 95% Wilson intervals
for dissatisfaction rates, with alpha = .01 for significance tests. The
analytic sample is completed responses passing a pre-treatment attention
check, the sole exclusion, applied before any analysis. A pre-analysis plan
containing the full analysis pipeline, run end-to-end on simulated data, is
attached.

Experimental Design Details

Not available

Randomization Method

Computer randomization: a randomizer element in the Qualtrics survey flow
presents the five arms evenly; assignment is stored in an embedded-data field.

Randomization Unit

individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

3,750 individuals (individual-level randomization; no clustering).

Sample size: planned number of observations

3,750 US adults recruited on Prolific; approximately 3,560 expected in the analytic sample after attention-check exclusions.

Sample size (or number of clusters) by treatment arms

750 individuals per arm: control, T1 (pre-selected default), T2 (opt-out
checkbox), T3 (inclusivity framing), T4 (environmental framing); approximately
712 per arm in the analytic sample.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Primary behavioral outcome (chose_veg, a proportion): with approximately 712 analytic observations per arm, alpha = .01 two-sided, and an assumed control uptake of 20%, power is 96.4% to detect a 10 percentage point gain (the smallest registered threshold) and effectively 100% for the 20 and 30 percentage point thresholds. Primary reactance outcome (dissat_chosen): by simulation (10,000 draws per arm), the probability that the 95% Wilson upper bound falls below the registered ceiling is 93-94% for T1, T3, and T4 (assumed true rate 15%, ceiling 20%) and 99.7% for T2 (assumed true rate 22%, ceiling 30%). The H1 criterion also requires the point estimate to meet the threshold, so the design is powered to certify effects comfortably above the thresholds, not effects exactly at them.

Supporting Documents and Materials