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Designing Warning Messages for Detecting Biased Online Product Recommendations: An Empirical Investigation

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  • Bo Xiao

    (Information Technology Management Department, Shidler College of Business, University of Hawai’i at Mānoa, Honolulu, Hawaii 96822)

  • Izak Benbasat

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

Abstract

The increasing adoption of product recommendation agents (PRAs) by e-commerce merchants makes it an important area of study for information systems researchers. PRAs are a type of Web personalization technology that provides individual consumers with product recommendations based on their product-related needs and preferences expressed explicitly or implicitly. Whereas extant research mainly assumes that such recommendation technologies are designed to benefit consumers and focuses on the positive impact of PRAs on consumers’ decision quality and decision effort, this study represents an early effort to examine PRAs that are designed to produce their recommendations on the basis of benefiting e-commerce merchants (rather than benefiting consumers) and to investigate how the availability and the design of warning messages (a potential detection support mechanism) can enhance consumers’ performance in detecting such biased PRAs. Drawing on signal detection theory, the literature on warning messages, and the literature on message framing, we identified two content design characteristics of warning messages—the inclusion of risk-handling advice and the framing of risk-handling advice—and investigated how they influence consumers’ detection performance. The results of an online experiment reveal that a simple warning message without accompanying advice on how to detect bias is a double-edged sword, because it increases correct detection of biased PRAs ( hits ) at the cost of increased incorrect detection ( false alarms ). By contrast, including in warning messages risk-handling advice about how to check for bias (particularly when the advice is framed to emphasize the loss from not following the advice) increases correct detection and, more importantly, also decreases incorrect detection. The patterns of findings are in line with the predictions of signal detection theory. With an enriched understanding of how the availability and the content design of warning messages can assist consumers in the context of PRA-assisted online shopping, the results of this study serve as a basis for future theoretical development and yield valuable insights that can guide practice and the design of effective warning messages.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orisre:v:26:y:2015:i:4:p:793-811
    DOI: 10.1287/isre.2015.0592
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    References listed on IDEAS

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    8. Weiquan Wang & Jingjun (David) Xu & May Wang, 2018. "Effects of Recommendation Neutrality and Sponsorship Disclosure on Trust vs. Distrust in Online Recommendation Agents: Moderating Role of Explanations for Organic Recommendations," Management Science, INFORMS, vol. 64(11), pages 5198-5219, November.
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