Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects

Gediminas Adomavicius (), Jesse C. Bockstedt (), Shawn P. Curley () and Jingjing Zhang ()
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Gediminas Adomavicius: Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
Jesse C. Bockstedt: Management Information Systems, Eller College of Management, The University of Arizona, Tucson, Arizona 85721
Shawn P. Curley: Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
Jingjing Zhang: Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405

Information Systems Research, 2013, vol. 24, issue 4, 956-975

Abstract: Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve accuracy of predictions, although behavioral aspects of using recommender systems are often overlooked. In our studies, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. We conducted three controlled laboratory experiments to explore the effects of system recommendations on preferences. Studies 1 and 2 investigated user preferences for television programs across a variety of conditions, which were surveyed immediately following program viewing. Study 3 investigated the granularity of the observed effects within individual participants. Results provide strong evidence that the rating presented by a recommender system serves as an anchor for the consumer's constructed preference. Viewers' preference ratings are malleable and can be significantly influenced by the recommendation received. The effect is sensitive to the perceived reliability of a recommender system and, thus, not a purely numerical or priming-based effect. Finally, the effect of anchoring is continuous and linear, operating over a range of perturbations of the system. These general findings have a number of important implications (e.g., on recommender systems performance metrics and design, preference bias, potential strategic behavior, and trust), which are discussed.

Keywords: anchoring effects; behavioral decision making; behavioral economics; electronic commerce; experimental research; preferences; recommender systems (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (36)

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