Disclosure, Verification, and the Value of Information in Gene-Edited Seafood: Cross-National Evidence on Consumer Preferences

Last registered on July 23, 2026

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0018986

Initial registration date

July 17, 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 23, 2026, 8:03 AM EDT

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

Locations

Country

Germany

Region

Country

Japan

Region

Country

United States of America

Region

Primary Investigator

Name

Gorm Kipperberg

Affiliation

University of Stavanger

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Andreea-Laura Cojocaru

PI Affiliation

University of Stavanger

Contact Investigator

PI Name

Julia Bronnmann

PI Affiliation

University of Southern Denmark

Contact Investigator

PI Name

Yuko Onozaka

PI Affiliation

University of Stavanger

Contact Investigator

Additional Trial Information

Status

In development

Start date

2026-07-20

End date

2026-12-31

Keywords

Agriculture, Behavior, Environment & Energy, Firms & Productivity, Governance, Other, Welfare

Additional Keywords

CRISPR, gene editing, salmon, gene-edited salmon, seafood, aquaculture, food labeling, disclosure, verification, information treatment, consumer preferences, consumer demand, discrete choice experiment, willingness to pay, stated preferences, survey experiment

JEL code(s)

C35, C90, D12, D83, Q13, Q18, Q51, Q57

Secondary IDs

Prior work

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

Abstract

This study examines how consumers in the United States, Japan, and Germany evaluate gene-edited farmed salmon when the use of gene editing via CRISPR technology is applied to induce fish sterility in order to reduce genetic mixing between escaped farmed salmon and wild salmon. The gene-editing status may be disclosed, unspecified, or third-party verified.
The study uses an online survey experiment with an embedded discrete choice experiment. Within each country, respondents are randomly assigned to a 2 × 3 factorial design: written general information brochure versus voiceover delivery of the same general information, crossed with no additional article, a positive article, or a negative article about gene-edited sterile farmed salmon. Respondents then complete choice tasks involving fresh salmon fillet alternatives that vary by production-method description, verification level, and price, with an option to choose none of the products.
The primary analysis uses the DCE to estimate preferences and willingness to pay for production-method, (non)disclosure, and verification. Secondary analyses examine post-DCE mechanism measures and an incentivized donation task. The planned sample is 3,000 completed eligible respondents per country, for a total of 9,000 respondents.

External Link(s)

Registration Citation

Citation

Bronnmann, Julia et al. 2026. "Disclosure, Verification, and the Value of Information in Gene-Edited Seafood: Cross-National Evidence on Consumer Preferences ." AEA RCT Registry. July 23. https://doi.org/10.1257/rct.18986-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Respondents are randomly assigned to receive general information about gene-edited sterile farmed salmon either as a written brochure or as a voiceover presentation of equivalent content. They are also randomly assigned to receive no additional article, a positive article, or a negative article about gene-edited sterile farmed salmon before completing a discrete choice experiment on salmon products.

Intervention Start Date

2026-07-20

Intervention End Date

2026-10-31

Primary Outcomes

Primary Outcomes (end points)

The primary outcome is the respondent’s selected alternative in each discrete choice experiment task. The main DCE-based outcomes are choices of CRISPR farmed salmon, conventional farmed salmon, unspecified farmed salmon, wild salmon, and the non-purchase option.

Primary Outcomes (explanation)

Primary outcomes will be analyzed using random utility models of DCE choices. From these models, we will estimate preference parameters, monetary welfare measures (e.g., willingness-to-pay, WTP), choice probabilities, substitution pattern measures, and projected market shares for salmon products varying by production-method description, verification level, and price.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary outcomes include post-DCE measures of beliefs about unspecified farmed salmon, stated valuation of CRISPR farmed salmon, labeling and verification preferences, perceived benefits, perceived naturalness, stated purchase likelihood for farmed salmon, wild salmon, and salmon overall, comprehension measures, and outcomes from an incentivized donation task.

Secondary Outcomes (explanation)

Secondary outcomes will be used to interpret the DCE estimates and examine potential mechanisms and heterogeneity. Donation-task outcomes include whether the respondent donates any positive amount, the amount donated from the lottery prize, and the selected donation cause. Attitudinal measures, including nature relatedness and food technology neophobia, may be used as moderators of DCE treatment effects and preference estimates.

Experimental Design

The study is an online randomized survey experiment with an embedded discrete choice experiment. Respondents are randomly assigned to one of six information conditions that vary the delivery mode of general information and the presence and valence of additional information about gene-edited sterile farmed salmon. After receiving the assigned information, respondents complete choice tasks involving salmon products that vary by production-method description, verification level, and price.

Experimental Design Details

Not available

Randomization Method

Computerized random assignment implemented by the survey platform or survey company. Randomization will be balanced within country across the six experimental cells.

Randomization Unit

Individual respondent. If the discrete choice experiment is administered in blocks, DCE block assignment will also be randomized at the individual respondent level.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

9,000 individual respondents. The treatment is not clustered; the individual respondent is the unit of randomization.

Sample size: planned number of observations

9,000 completed eligible respondents: 3,000 in the United States, 3,000 in Japan, and 3,000 in Germany.

Sample size (or number of clusters) by treatment arms

The study has six treatment arms in each country, with a target of 500 completed eligible respondents per arm per country:

Written brochure + no additional article: 500 per country; 1,500 total
Written brochure + positive article: 500 per country; 1,500 total
Written brochure + negative article: 500 per country; 1,500 total
Voiceover + no additional article: 500 per country; 1,500 total
Voiceover + positive article: 500 per country; 1,500 total
Voiceover + negative article: 500 per country; 1,500 total

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

The treatment is not clustered; the individual respondent is the unit of randomization. The planned sample of 9,000 completed eligible respondents, with 3,000 respondents per country and balanced assignment across six experimental cells, provides adequate power to detect small-to-moderate treatment effects in the main analyses. For simple treatment-arm comparisons, the minimum detectable effect size is expected to be in the range of approximately 0.10–0.20 standard deviations, depending on whether analyses are pooled across countries or conducted separately by country. For binary choice outcomes, this corresponds approximately to effects in the range of 5–10 percentage points under conservative assumptions. The main DCE analysis uses repeated choice observations per respondent and random utility models with standard errors accounting for repeated choices by the same respondent.

Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name

Institutional review board - UiS School of Business and Law (HHUiS-IRB)

IRB Approval Date

2026-07-03

IRB Approval Number

HHUiS-IRB-2026-006

Analysis Plan

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