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Where Did They Go Wrong? An Analysis of the Failure of Knowledgeable Internet Consumers to Detect Deception Over the Internet

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  • Stefano Grazioli

    (McIntire School of Commerce at the University of Virginia)

Abstract

This paper uses an information-processing model of deception detection to understand the reasons underlying Internet consumers' success and failure at detecting forms of intentional deception that occur on the Internet. Eighty MBA students visited either a real commercial site or a deceptive copycat (“page-jacking”) site. The deceptive site was identical to the clean site except that it contained six deceptive manipulations (e.g., forged favorable quotes from authoritative sources). This study compares the information processing behavior of four groups of subjects: those who detected the deception, those who missed it, those who correctly identified the real site as non-deceptive, and those who incorrectly believed that the real site was deceptive. It was found that (1) priming subjects to generate the hypothesis of deception weakly facilitates detection success, (2) competence at evaluating the hypothesis of deception is a strong differentiator between successful and unsuccessful detectors, and (3) successful detectors rely on “assurance” cues and heavily discount “trust” cues. Unsuccessful detectors do the opposite.

Suggested Citation

  • Stefano Grazioli, 2004. "Where Did They Go Wrong? An Analysis of the Failure of Knowledgeable Internet Consumers to Detect Deception Over the Internet," Group Decision and Negotiation, Springer, vol. 13(2), pages 149-172, March.
    • Handle: RePEc:spr:grdene:v:13:y:2004:i:2:d:10.1023_b:grup.0000021839.04093.5d
    DOI: 10.1023/B:GRUP.0000021839.04093.5d
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    References listed on IDEAS

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    1. Johnson, Paul E. & Grazioli, Stefano & Jamal, Karim, 1993. "Fraud detection: Intentionality and deception in cognition," Accounting, Organizations and Society, Elsevier, vol. 18(5), pages 467-488, July.
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    Cited by:

    1. Bo Xiao & Izak Benbasat, 2015. "Designing Warning Messages for Detecting Biased Online Product Recommendations: An Empirical Investigation," Information Systems Research, INFORMS, vol. 26(4), pages 793-811, December.
    2. Sirkka L. Jarvenpaa & Ann Majchrzak, 2010. "Research Commentary ---Vigilant Interaction in Knowledge Collaboration: Challenges of Online User Participation Under Ambivalence," Information Systems Research, INFORMS, vol. 21(4), pages 773-784, December.
    3. Yang, Jinbi & Sia, Choon Ling & Ou, Carol, 2015. "Identify the antecedents of distrust in a website," Other publications TiSEM 27db9390-f2e0-4006-ab0c-9, Tilburg University, School of Economics and Management.
    4. Kim Kaivanto, 2014. "The Effect of Decentralized Behavioral Decision Making on System‐Level Risk," Risk Analysis, John Wiley & Sons, vol. 34(12), pages 2121-2142, December.
    5. Ryan Wright & Suranjan Chakraborty & Asli Basoglu & Kent Marett, 2010. "Where Did They Go Right? Understanding the Deception in Phishing Communications," Group Decision and Negotiation, Springer, vol. 19(4), pages 391-416, July.
    6. Xin Wen & Liang Xu & Jie Wang & Yuan Gao & Jiaming Shi & Ke Zhao & Fuyang Tao & Xiuying Qian, 2022. "Mental States: A Key Point in Scam Compliance and Warning Compliance in Real Life," IJERPH, MDPI, vol. 19(14), pages 1-16, July.
    7. Jeanne Logsdon & Karen Patterson, 2009. "Deception in Business Networks: Is It Easier to Lie Online?," Journal of Business Ethics, Springer, vol. 90(4), pages 537-549, December.
    8. Gontar, Patrick & Homans, Hendrik & Rostalski, Michelle & Behrend, Julia & Dehais, Frédéric & Bengler, Klaus, 2018. "Are pilots prepared for a cyber-attack? A human factors approach to the experimental evaluation of pilots' behavior," Journal of Air Transport Management, Elsevier, vol. 69(C), pages 26-37.
    9. Isabel Riquelme & Sergio Román, 2014. "The Influence of Consumers’ Cognitive and Psychographic Traits on Perceived Deception: A Comparison Between Online and Offline Retailing Contexts," Journal of Business Ethics, Springer, vol. 119(3), pages 405-422, February.
    10. Liuchang Xu & Jie Wang & Dayu Xu & Liang Xu, 2022. "Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization," IJERPH, MDPI, vol. 19(1), pages 1-12, January.

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