质量(理念)
订单(交换)
产品(数学)
广告
现存分类群
业务
营销
经济
变化(天文学)
微观经济学
哲学
物理
几何学
数学
认识论
财务
进化生物学
天体物理学
生物
作者
Steven N. Wiggins,W. J. Lane
摘要
consumers. In this paper, we begin to remedy this omission by developing a formal model that shows how risk affects the decisions of riskaverse consumers regarding the amount of search and whether or not to buy advertised products. We incorporate risk into the model by assuming that consumers are concerned not only with expected product quality, but also with the amount of variation that exists across the qualities available. The risk arises not because the quality is itself variable, but rather because there is variation in quality across products, and consumers cannot evaluate the quality of a particular brand before purchase. We consider two sources of information about quality, both of which are costly to consumers: firms supply consumers with implicit quality signals through advertisements that convey only that a product is advertised but not direct information about the quality of the product, and consumers supply themselves with direct quality information through search.' These methods affect both the expected value of quality received and its riskiness. We analyze search and advertising simultaneously in order to model the consumers' substitution of one information source for the other. We then use the model to show how firms may use advertising as an implicit signal that advertised products are less risky than unadvertised products and to explain why information is primarily provided by firms in some markets, whereas in other markets, consumers acquire their information through search. In addition to explicitly considering risk, our model has several other attributes that distinguish it from the extant search literature. In order to obtain a meaningful market equilibrium, we construct a full partial-equilibrium model in which the behavior of both buyers and sellers is examined and the distribution of qualities in the market is endogenously determined. This approach is used
科研通智能强力驱动
Strongly Powered by AbleSci AI