JOURNAL ARTICLE

Amplifying Consumers' Voice: The Federal Trade Commission's Report Fraud Website Redesign.

  • Published In: Marketing Science (INFORMS), 2025, v. 44, n. 3. P. 525 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Grosz, Michel; Raval, Devesh 3 of 3

Abstract

This article analyzes the impact of a 2020 redesign of the Federal Trade Commission's (FTC) consumer fraud complaint website, ReportFraud.ftc.gov, which aimed to reduce the hassle costs and increase the perceived public good benefits of filing complaints. Using a regression discontinuity design, the study finds that the redesign led to a 28% increase in completed online complaints, driven primarily by higher complaint completion rates rather than more users visiting the site. The redesign also improved the quality of complaints by encouraging more detailed consumer information but resulted in shorter, simpler complaint narratives, suggesting that less sophisticated or more vulnerable consumers were induced to complain. Additionally, the increase was concentrated in complaints about telemarketing and imposter scams—frauds where many consumers are exposed but fewer report monetary losses—indicating a rise in altruistically motivated reporting. The study observes relatively small differences in complaint increases across demographic groups but notes more complaints from communities previously less likely to report fraud.

Additional Information

  • Source:Marketing Science (INFORMS). 2025/05, Vol. 44, Issue 3, p525
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2025
  • ISSN:0732-2399
  • DOI:10.1287/mksc.2023.0643
  • Accession Number:187706428
  • Copyright Statement:Copyright of Marketing Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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