Should Recommendation Agents Think Like People?

Abstract

Electronic recommendation agents have the potential to increase the level of service provided by firms operating in the online environment. Recommendation agents assist consumers in making product decisions by generating rank-ordered alternative lists based on consumer preferences. However, many of the online agents currently in use rank options in different ways than the consumers they are designed to help. Two experiments examine the role of similarity between an electronic agent and a consumer, in terms of actual similarity of attribute weights and perceived similarity of decision strategies, on the quality of consumer choices. Results indicate that it helps consumers to use a recommendation agent that thinks like them, either in terms of attribute weights or decision strategies. When agents are completely dissimilar, consumers may be no better, and sometimes worse off, using an agent’s ordered list than if they simply used a randomly ordered list of options.