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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Rebuilding Trust: E-commerce Efforts to Overcome Fraud Impacts and Enhance Consumer Loyalty

Authors: Halim, Erwin; Rafli, Muhammad; Teng, Angelina Hartanto;

Rebuilding Trust: E-commerce Efforts to Overcome Fraud Impacts and Enhance Consumer Loyalty

Abstract

E-commerce has transformed the global marketplace, offering convenience but also increasing fraud risks such as payment fraud, account takeovers, and fake reviews, which erode consumer trust and affect brand loyalty. This study examines the impact of fraud on consumer confidence and evaluates mitigation strategies used by e-commerce platforms.Based on a survey of 450 respondents from Jakarta (55%), Surabaya (20%), Bandung (15%), and Medan (10%), the sample includes 52% male and 48% female, aged 18-45 years. Participants were selected for their active engagement in online shopping.Key fraud prevention measures analyzed include AI-powered detection systems, blockchain for secure transactions, and biometric authentication. Trust-building strategies such as transparent return policies, improved customer service, and security notifications are also assessed. Additionally, the study explores the role of regulations and industry collaboration in combating fraud.Through case studies and empirical research, this paper emphasizes a multi-layered security approach and customer-centric recovery strategies to restore trust and ensure long-term consumer loyalty. The findings offer practical insights for businesses enhancing fraud prevention while maintaining a positive user experience.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average