Recommending or persuading? The impact of a shopping agent's algorithm on user behavior

Gerald Häubl, Kyle B. Murray

Research output: Contribution to conferencePaperpeer-review

27 Citations (Scopus)


This paper investigates the potential of recommendation agents for electronic shopping to influence human decision making by shaping user preferences. Specifically, we examine how the type of information that is elicited by a shopping agent for use in its recommendation algorithm may affect consumers' preference for product features and ultimately their product choice in an electronic marketplace. A recommendation agent is defined as a software tool that (a) calibrates a model of a user's preference based on his/her input and (b) uses this model to make personalized product recommendations. We report the results of a controlled experiment that demonstrates that, everything else being equal, the inclusion of a product feature in a recommendation agent renders this feature more prominent in shoppers' purchase decisions. In addition, we find that this effect is moderated by an important property of the marketplace - the correlation structure among the features of available products. We conclude that electronic shopping agents, through the design of their recommendation algorithms, have the potential to influence user preferences in a systematic fashion.

Original languageEnglish
Number of pages8
Publication statusPublished - 1 Dec 2001
EventEC'01: Proceedings of the 3rd ACM Conference on Electronic Commerce - Tampa, United States of America
Duration: 14 Oct 200117 Oct 2001


ConferenceEC'01: Proceedings of the 3rd ACM Conference on Electronic Commerce
Country/TerritoryUnited States of America


  • Consumer behavior
  • Human decision making
  • Influence
  • Online shopping
  • Personalization
  • Persuasion
  • Product choice
  • Recommendation systems
  • Shopping agents
  • User preferences

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