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

Last registered on June 22, 2026

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

Region

Primary Investigator

Affiliation
Kochi University of Technology

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2026-06-17
End date
2026-07-31
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
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Experimental Details

Interventions

Intervention(s)
type-assignment
Intervention (Hidden)
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
Main part:
There are three quality-types; type-20, type-40, or type-60.
Each quality-type (type-20, type-40, or type-60) will receive a good signal labeled as a ‘“Good” rating with probability 20%, 40%, or 60%, respectively, depending on the quality type, or receive a bad signal labelled as a ‘“Bad” rating with the remaining probability (e.g., type-20 will receive a “Good” rating with probability 20% and a “Bad” rating with probability 80%).
Each individual is assigned to one of the three treatments: “20-40” treatment, “40-60” treatment, or “20-60” treatment. In “X-Y” treatment, half the subjects are assigned to type-X and the other half to type-Y. The assigned type is fixed throughout the session.
The above process results in the following 6 conditions for each individual:
- type-20, with type-40 players in the same session
- type-20, with type-60 players in the same session
- type-40, with type-20 players in the same session
- type-40, with type-60 players in the same session
- type-60, with type-20 players in the same session
- type-60, with type-40 players in the same session
The revenue for each player depends on the realization of their own signal (“Good” rating or “Bad” rating), but not others.
A player will receive 105,000 ECU from “Good” rating, and 75,000 ECU from “Bad” rating.
Each player can increase the probability of the “Good” rating by up-to 40%. This is regarded as the “effort level” in the “primary outcomes” section in this pre-registration.
Such an increase is costly. The costs to increase the probability of the “Good” rating are quadratically increasing.
An increase of the probability by r% costs (5 times r^2) ECU.
The decisions are repeated for 24 rounds.
One out of 24 rounds will be randomly selected as a payoff-relevant round if this part is used for participants’ reward.

Risk survey:
A simple Holt-Laury style risk survey, consisting of 11 questions.
In each question, each participant chooses option A or option B.
In each question, if option A is chosen, the player will receive 150,000 ECU with probability 50%, and 50,000 ECU with probability 50%.
In question n, if option B is chosen, the player will receive (40,000 + n times 10,000) ECU for sure.
One out of 11 questions will be randomly selected as a payoff-relevant question if this part is used for participants’ reward.

Demographic/Experience survey:
In addition to demographic questions such as gender, age, university, and major, we also ask about participants’ experience of e-commerce websites, along with a qualitative survey on the experiment.
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)
IRB

Institutional Review Boards (IRBs)

IRB Name
Kochi University of Technology
IRB Approval Date
2025-03-11
IRB Approval Number
259-C1

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