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Abstract:
A proposed method of combating obesity in the United States is to hold food companies legally liable for obesity-related damages. Recent lawsuits against fast-food restaurants, such as Pelman v. McDonald's Corp., have tried to draw on the success of tobacco litigation by claiming that fast-food marketers provide misleading information about the nutritional value of their products, leading consumers to overconsume and, thus, become obese. The Pelman plaintiffs also allege that fast food is addictive and have asked to represent a class of children who have become obese as a result of McDonald's products. This article compares legal efforts against the aggressive marketing of fast food with those against the marketing of tobacco products and argues that, for several reasons, such legal efforts will face substantial hurdles. In particular, this article argues that to be successful, such lawsuits must show either that McDonald's acted deceptively or that it has a duty to warn consumers about the unhealthful nature of its products. In addition, they must show that they satisfy the requirements necessary to certify a class. Finally, they must gain public and legislative support for legal action. Without these lawsuits satisfying the necessary legal elements and gaining increased public support for legal action against the industry, it seems unlikely that the fast-food industry will be held responsible for obesity-related damages. Having concluded that the tobacco litigation experience does not offer a promising road map to combat obesity, the authors briefly consider the vulnerability of the food industry to alternative legal strategies and legislative actions.

Fast Food, Lawsuits, Tobacco, Marketing and Society, Policy

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.

Decision-Making, Electronic Commerce, Recommendation Agents, Personalization, Information Search, Latent Class Model