Seller's Competitive (Dis)advantage in the Market and Manipulation of Own Reputation: A Laboratory Experiment

Last registered on June 22, 2026
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Pre-Trial

Trial Information

General Information

Title
Seller's Competitive (Dis)advantage in the Market and Manipulation of Own Reputation: A Laboratory Experiment
RCT ID
AEARCTR-0018947
Initial registration date
June 16, 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
June 22, 2026, 6:53 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
Yuta Yasui
Affiliation
Kochi University of Technology
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2026-06-17
End date
2026-07-31
Keywords
Lab
Additional Keywords
Reputation, Ratings, Manipulation, Fake reviews, Reference point
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We investigate which characteristics of sellers induce greater effort to enhance their reputation. In particular, we focus on (i) the absolute quality of the goods a seller deals in, and (ii) the relative quality of those goods compared with the goods dealt in by another seller in the market. In the experiment, we randomly assign the quality-types to sellers. One of them is assigned to a relatively high-quality type, and another is assigned to a relatively low-quality type. The absolute quality-type determines the default signal distribution, and the signal realization determines the revenue of the player. Each player can manipulate their own signal distribution, but not others, by paying some costs. We investigate whether the own quality-type and/or relative position in the session matters for the manipulative behavior, even if the monetary marginal gains from manipulating the signals are common among different quality-types.
External Link(s)

Registration Citation

Citation
Yasui, Yuta. 2026. "Seller's Competitive (Dis)advantage in the Market and Manipulation of Own Reputation: A Laboratory Experiment." AEA RCT Registry. June 22. https://doi.org/10.1257/rct.18947-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
type-assignment
Intervention Start Date
2026-06-17
Intervention End Date
2026-07-31

Primary Outcomes

Primary Outcomes (end points)
effort levels to change the signal distribution
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
None
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Show-up fee: 1500JPY (If necessary, we may raise the show-up fee to recruit a sufficient number of participants.)
Either “main part” or “risk survey” is randomly chosen with equal probability as a payoff-relevant part.
Experimental currency units (ECU) will be rewarded to participants depending on their outcome in the payoff-relevant part.
Participants will receive their rewards in cash (JPY), depending on the ECU they are rewarded during the experiment.
Experimental Design Details
Not available
Randomization Method
Step 1: Each session is assigned to one of three treatments (“20-40”, “20-60”, or “40-60”), which is hidden from participants before the session. In order to collect 40 participants for each treatment, we assign the treatment in an ad hoc manner, but without knowing the participants’ characteristics.

Step 2: After the session starts, each participant is assigned to a quality-type (type-20, type-40, or type-60), based on their ID number during the session, randomly generated by otree. If the ID number is odd, a relatively high-quality type in the session is assigned. Otherwise, a relatively low-quality type in the session is assigned.

Payoff: During a session, otree app ("random" module in Python) secretly and randomly chooses “main part” or “risk survey” with equal probabilities, and chooses a round/question number as a payoff-relevant round/question.
Randomization Unit
Step 1: Session level
Step 2: Participant level
Payoff: Session level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
120 individuals
Sample size: planned number of observations
120 individuals
Sample size (or number of clusters) by treatment arms
40 individuals for “20-40” treatment
40 individuals for “20-60” treatment
40 individuals for “40-60” 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
Kochi University of Technology
IRB Approval Date
2025-03-11
IRB Approval Number
259-C1
Analysis Plan