Strategies for Reducing Non-Institutional Fraud and Building Trust in a Digital Market Platform: A Behavioral Lab Experiment in Nigeria

Last registered on July 26, 2022


Trial Information

General Information

Strategies for Reducing Non-Institutional Fraud and Building Trust in a Digital Market Platform: A Behavioral Lab Experiment in Nigeria
Initial registration date
July 22, 2022

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 26, 2022, 1:51 PM EDT

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



Primary Investigator

University of Pennsylvania

Other Primary Investigator(s)

PI Affiliation
Trinity College Dublin
PI Affiliation
Trinity College Dublin
PI Affiliation
Busara Center for Behavioral Economics

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
The high prevalence of digital financial fraud makes it difficult for businesses to distinguish between real communications from digital service providers and fraudulent communication. This could lead to a lack of trust and usage in digital financial services. We test two strategies for preventing non-institutional fraud: an anti-fraud campaign and a technical intervention – a unique communications code – which verifies the provenance of messages sent from a digital platform. Using a behavioral laboratory experiment along with survey outcomes, we test whether anti-fraud campaigns increase the ability of MSMEs to distinguish between fraudulent and legitimate communications. Additionally, we test how confidence, trust, and usage of digital financial services respond to anti-fraud campaigns. Finally, we test that the deployment of the unique communications code is suitable for businesses using a follow-up experimental exercise.
External Link(s)

Registration Citation

Byrne, Shane et al. 2022. "Strategies for Reducing Non-Institutional Fraud and Building Trust in a Digital Market Platform: A Behavioral Lab Experiment in Nigeria." AEA RCT Registry. July 26.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Accurate identification of fraudulent scenarios, Confidence in identification ability, Reported likeliness of using digital financial services in the future, trust in digital financial services
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Before the experiment, participants will take a short survey. Our lab partner will randomize participants into either a control group or one of three treatment groups. Each of the three treatment groups receives a variation of an educational intervention aiming at helping participants distinguish between genuine and fraudulent communication. After the educational intervention is received, participants will complete a task that requires them to determine whether an example communication is fraudulent or not. After this, a second very short survey is administered. Finally, participants will be re-randomized into two groups to receive a unique communication code. The first group will choose their own code and the other will be assigned one. After the end of the experiment, the control group will then receive the educational intervention. Finally, we will follow up with a task that assesses the feasibility of the unique communications code as a security device.
Experimental Design Details
Randomization Method
Participants will be randomly selected to attend sessions, which will feature a single treatment or control. The sessions will be randomly ordered within blocks. All randomization will take place via computer.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
750 Micro, Small, and Medium Enterprises (MSMEs)
Sample size: planned number of observations
750 MSMEs
Sample size (or number of clusters) by treatment arms
750 participants will be evenly allocated across 4 cells (An average of 187.5 participants per cell - including a control group and three treatment groups).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Trinity College Dublin Faculty of Arts, Humanities and Social Sciences Research Ethics Committee
IRB Approval Date
IRB Approval Number
IRB Name
Ahmadu Bello University Ethics Committee on Use of Human Subjects for Research
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan

MD5: 288484eac7b1c2a4f1ea0589f6033665

SHA1: 4275f0c9120808ecc796d3107cd915ee34838b34

Uploaded At: July 22, 2022


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials