Abstract
In industries with perishable goods dynamic pricing schemes are often used and many models have been proposed to maximize seller’s revenues. These models are based on a range of assumptions about how customers make buying decisions. Some assume that customers are myopic and are not forward looking while others assume that customers are strategic and anticipate future prices. In the second stream, a standard assumption is that strategic customers maximize their expected utility. All these models predict that customers will purchase in the current period if and only if their valuation exceeds some threshold. We use laboratory experiments to gain insights into how customers make purchase decisions when they have the option of buying at a higher price or waiting for a lower price but incur the risk of the product being out of stock. We find that the quantal response model (QRM), a quasi-rational model, provides a more accurate description of customers’ decisions. We also explore how customers learn in these types of settings.
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