Product-Related Deception in E-Commerce: A Theoretical Perspective1
Abstract
With the advent of e-commerce, the potential of new Internet technologies to mislead or deceive consumers has increased considerably. This paper extends prior classifications of deception and presents a typology of product-related deceptive information practices that illustrates the various ways in which online merchants can deceive consumers via e-commerce product websites. The typology can be readily used as educational material to promote consumer awareness of deception in e-commerce and as input to establish benchmarks for good business practices for online companies. In addition, the paper develops an integrative model and a set of theory-based propositions addressing why consumers are deceived by the various types of deceptive information practices and what factors contribute to consumer success (or failure) in detecting such deceptions. The model not only enhances our conceptual understanding of the phenomenon of product-based deception and its outcomes in e-commerce but also serves as a foundation for further theoretical and empirical investigations. Moreover, a better understanding of the factors contributing to or inhibiting deception detection can also help government agencies and consumer organizations design more effective solutions to fight online deception.
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Deepfakes can be used to deceive the marketplace by manipulating information content, presentation, and generation.
“Similar to the arguments presented by Xiao and Benbasat (2011), deepfakes can be used to deceive the marketplace by manipulating information content, presentation, and generation.”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Xiao and Benbasat (2011)
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- Authors
- Xiao, B., & Benbasat, I.
- Affiliations
- Hong Kong Baptist University (HK)
- University of British Columbia (CA)
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2011-03-01
- Volume
- 35
- Issue
- 1
- Pages
- 169-195
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W1576800334
- Cited by
- 265
- References
- 118
- Citations by year
- 2026: 14 · 2025: 10 · 2024: 17 · 2023: 27 · 2022: 22 · 2021: 20 · 2020: 19 · 2019: 17 · 2018: 15 · 2017: 11 · 2016: 27 · 2015: 15 · 2014: 22 · 2013: 12 · 2012: 12
- FWCI
- 44.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- MIS Quarterly (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- e-commerce, consumer deception, deception detection, consumer awareness, online merchants, consumer protection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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