Effects of Fraud on Digital Services Use

Last registered on July 13, 2026
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Pre-Trial

Trial Information

General Information

Title
Effects of Fraud on Digital Services Use
RCT ID
AEARCTR-0019148
Initial registration date
July 10, 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 13, 2026, 8:31 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
Elif Kubilay
Affiliation
University of Essex
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Other Primary Investigator(s)

PI Name
Lisa Spantig
PI Affiliation
RWTH Aachen University and University of Essex
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PI Name
Eva Raiber
PI Affiliation
Aix-Marseille University
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PI Name
Laura Nelima Barasa
PI Affiliation
University of Nairobi
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Additional Trial Information

Status
In development
Start date
2026-07-15
End date
2027-02-28
Keywords
Behavior, Finance & Microfinance
Additional Keywords
Consumer protection, consumer fraud, digital financial services, scam susceptibility, scam prevalence
JEL code(s)
D14, D18, G53, O12
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The expansion of digital financial services in developing countries has brought substantial economic benefits but has also led to an increase in consumer protection problems. While previous research has focused on the direct costs of scams, less is known about how fraud perceptions influence broader digital engagement. We study two channels through which these perceptions affect behavior: beliefs about the prevalence of scams (the environment channel) and beliefs about one's own ability to detect scams (the self channel). In a large-scale randomized online experiment with Kenyan respondents, we experimentally vary these beliefs using objective information on scam prevalence and personalized feedback on scam identification ability. We then measure effects on beliefs, digital service use, responses to genuine and fraudulent SMS messages, demand for scam prevention training, consumer protection engagement, and trust in digital institutions. We expect the findings to provide new evidence on how fraud perceptions shape digital participation and inform the design of consumer protection policies.
External Link(s)

Registration Citation

Citation
Barasa, Laura Nelima et al. 2026. "Effects of Fraud on Digital Services Use ." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.19148-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
To study how fraud perceptions influence digital behavior, we implement a large-scale online experiment with Kenyan respondents. Participants are randomly assigned to one of five experimental arms that vary perceptions of scam prevalence and susceptibility to fraud. The interventions are designed to study two distinct channels. The first is the prevalence (environment) channel, whereby beliefs about the frequency of scam attempts influence trust in and adoption of digital services. The second is the susceptibility (self) channel, whereby beliefs about one's own ability to identify scams shape perceived vulnerability and subsequent digital behavior. Further details on the intervention are provided in the pre-analysis plan.
Intervention Start Date
2026-07-15
Intervention End Date
2026-08-07

Primary Outcomes

Primary Outcomes (end points)
(1) Expected Usage of Digital Services, captured by (1.1) Digital Service Use Index and (1.2) Digital Platform Engagement Index, (2) Adoption of Digital Services and (3) Take-up of Digital Opportunities
Primary Outcomes (explanation)
(1) Expected Usage of Digital Services: We measure expected usage of digital services using two indices defined as follows:

(1.1) Digital Service Use Index: Respondents report their likelihood of using a range of digital financial services in the near future. We combine these responses into a summary measure of intended digital service use.

(1.2) Digital Platform Engagement Index: We measure respondents' intended engagement with a range of digital platforms and services using an aggregated index based on self-reported likelihood of future use. In a follow-up survey, we additionally collect information on respondents' actual use of digital services, allowing us to measure changes in digital engagement at both the extensive and intensive margins.

(2) Adoption of Digital Services: We measure willingness to adopt digital financial services using an incentive-compatible choice task in which respondents make repeated choices between alternative payment methods. We construct measures capturing respondents' willingness to switch to a digital financial service. In a follow-up survey, we additionally measure self-reported adoption of new digital financial services during the study period.

(3) Take-up of Digital Opportunities: After the main survey, respondents may receive a follow-up SMS offering a genuine digital opportunity. We use respondents' engagement with this opportunity to construct behavioral measures of digital participation, including whether they engage with the opportunity and whether they complete the associated activity.

Secondary Outcomes

Secondary Outcomes (end points)
We examine two groups of secondary outcomes. The first consists of policy-oriented outcomes, including reporting behavior, demand for fraud prevention, support for consumer protection measures, responsible consumer behavior, and responses to fraud attempts. The second consists of mechanism outcomes, including posterior beliefs about scam identification ability, scam prevalence, and the likelihood of experiencing fraud-related losses; perceived consequences of victimization; perceived trustworthiness of digital services; responses to suspected scam messages; and digital platform engagement by perceived platform risk.
Secondary Outcomes (explanation)
(1) Policy-Oriented Outcomes

(1.1) Reporting Behavior: We measure respondents' stated willingness to report fraudulent activity through formal channels. This outcome is collected both immediately after the intervention and in the follow-up survey.

(1.2) Demand for Fraud Prevention: We measure respondents' willingness to obtain fraud prevention resources using an incentive-compatible elicitation.

(1.3) Support for Consumer Protection: We measure support for consumer protection initiatives using an incentivized behavioral measure.

(1.4) Responsible Consumer Behavior: We construct an index capturing respondents' engagement in behaviors that promote online safety and consumer protection. This outcome is collected in the follow-up survey.

(1.5) Response to Fraud Attempts: We measure respondents' behavioral responses to a fraud attempt using real-world engagement outcomes.

(2) Mechanism Outcomes

(2.1) Posterior Beliefs about Scam Identification Ability: We measure respondents' beliefs about their own ability to identify fraudulent communications. This outcome is collected immediately after the intervention and in the follow-up survey.

(2.2) Posterior Beliefs about Scam Prevalence: We measure respondents' beliefs about the prevalence of scam attempts in their environment. This outcome is collected immediately after the intervention and in the follow-up survey.

(2.3) Posterior Beliefs about Fraud Risk: We measure respondents' perceived likelihood of personally experiencing fraud.

(2.4) Perceived Consequences of Victimization: We measure respondents' perceptions of the financial and non-financial consequences of becoming a fraud victim using a standardized index. This outcome is also collected in the follow-up survey.

(2.5) Perceived Trustworthiness of Digital Services: We construct an index capturing respondents' trust in digital services and institutions responsible for protecting users. This outcome is collected both immediately after the intervention and in the follow-up survey.

(2.6) Responses to Suspected Fraudulent Messages: We measure intended responses to potentially fraudulent communication, including cautious information processing and engagement.

(2.7) Digital Platform Engagement by Perceived Risk: We examine intended engagement with digital platforms that differ in their perceived level of fraud risk.

Experimental Design

Experimental Design
The study consists of a large-scale online randomized experiment administered to a panel of Kenyan adults. Approximately 3,500 respondents are randomly assigned at the individual level to one of five experimental arms. The five arms include a pure control condition and four intervention conditions that vary two dimensions of fraud perceptions. One set of interventions provides respondents with feedback designed to update beliefs about their own ability to identify fraudulent messages (high susceptibility vs. low susceptibility). The other set provides information designed to update beliefs about the prevalence of scam attempts in Kenya (high prevalence vs. low prevalence). The treatments are designed to isolate the effects of these two channels on subsequent beliefs and digital behavior. Following the treatments, all respondents complete a common set of outcome measures capturing beliefs, digital service use, responses to genuine and fraudulent messages, consumer protection behavior, and trust in digital services. Full details on the treatment implementation, randomization, and information provided to respondents are included in our pre-analysis plan.
Experimental Design Details
Not available
Randomization Method
Randomization is done within Qualtrics
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Treatment is not clustered in this study.
Sample size: planned number of observations
3500
Sample size (or number of clusters) by treatment arms
(1) Pure Control: app. 700 respondents

(2) Low susceptibility arm: app. 700 respondents

(3) High susceptibility arm: app. 700 respondents

(4) Low prevalence information: app. 700 respondents

(5) High prevalence information: app. 700 respondents
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Please see Section 7 in our Pre-Analysis Plan.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Essex
IRB Approval Date
2025-09-22
IRB Approval Number
ETH2526-0066
Analysis Plan

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