Designing Warning Messages for Detecting Biased Online Product Recommendations: An Empirical Investigation Bo Xiao Information Technology Management Department, Shidler College of Business, University of Hawaii at Manoa, Honolulu, HI, United States,
[email protected] Izak Benbasat Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, Canada,
[email protected] 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. While 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 benefitting 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 two-edged sword, as it increases correct detection of biased PRAs (hits) at the cost of increased incorrect detection (false alarms).