Competing With Data & Analytics
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Electronic information can easily overwhelm people with large volumes of data. An abundance of information often strains human limits: attention, memory, motivation or other factors. In response to this challenge, software that assists humans in filtering and organizing information into more digestible amounts and formats have appeared (Alba et al., 1997; Häubl and Trifts, 2000; Todd and Benbasat, 1999, 2000). Real-world examples of such electronic decision aids include ePocrates, which provides pharmacists with valuable summary information on drug interactions through a personal digital assistant and news Web sites that allow users to personalize the presentation of weather information. These decision aids are altruistic in the sense that they have no vested interest in what the user does with the information. Similar applications exist in many markets to help buyers make complex purchase decisions. For example, in Canada, MLS.ca screens houses for sale based on the buyer’s preferences for location, price, size and other features.
However, not all decision aids are altruistic. Indeed, many are designed not just to assist buyers but also to steer them in a particular direction. This makes them “double agents.” Over and above helping buyers make choices, they act on behalf of sellers to influence buyer behavior. Examples of such decision aids exist at a number of vendor sites including Amazon.com’s “Your Store,” GM’s “Auto Choice Advisor,” and IBM’s “Solution Profiler.”
This article focuses on electronic agents that provide online shoppers with personalized product recommendations and the benefits and potential difficulties consumers may experience when using such decision aids. An electronic recommendation agent is a decision aid that 1) helps the consumer understand his or her preference, in terms of product attributes, based on a learning phase during which the consumer reveals subjective preference information to the agent; and 2) makes personalized recommendations in the form of a sorted list of products based on its understanding of the consumer’s preference (Ariely et al., 2004; Häubl and Trifts, 2000).
The Benefits Of Agent-Assisted Shopping
Most of us have relied on the recommendation of a sales clerk in a department store, an auto mechanic or someone else who had a vested interest in what we buy. We realize that these people may be biased. Nevertheless, we think their input can be worthwhile. They can save us time and energy and help us to avoid problems. Similarly, consumers rely on electronic recommendation agents to help them make better decisions with less effort.
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