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Cited by 581

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Perils of Internet fraud: an empirical investigation of deception and trust with experienced Internet consumers

Grazioli, S., Jarvenpaa, S.L.·2000·IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
DOI 10.1109/3468.852434
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Abstract

This study examines consumer evaluations of a real commercial web site and a fraudulent site that imitates it. The forged site contains malicious manipulations designed to increase trust in the site, decrease perceived risk, and ultimately increase the likelihood that visitors would buy from it. Besides measuring the consumer's willingness to buy from the site, this study recorded the actual ordering of a laptop. Results show that most subjects failed to detect the fraud manipulations, albeit a few succeeded. The fraud has the effect of increasing the consumers' reliance in assurance mechanisms and trust mechanisms, which in turn decrease perceived risk and increase trust in the store. The study confirms hypothesized relationships between purchase behavior, willingness to buy, attitudes toward the store, risk, and trust that are consistent with other trust models found in the literature. Overall, the study sheds light on consumers' vulnerability to attack by hackers posing as a legitimate site.

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Citations

Cites

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Metadata

Authors
Grazioli, S., & Jarvenpaa, S. L.
Venue
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2000-07
Volume
30
Issue
4
Pages
395-410
ISSN
1083-4427 (ISSN-L)
DOI
10.1109/3468.852434
OpenAlex
W2135746613
Cited by
581
References
11
FWCI
25.06
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.