With the proliferation of social media, consumers’ cognitive costs during information-seeking can become non-trivial during an online shopping session. We propose a dynamic structural model of limited consumer search that combines an optimal stopping framework with an individual-level choice model. We estimate the parameters of the model using a dataset of approximately 1 million online search sessions resulting in bookings in 2117 U.S. hotels. The model allows us to estimate the monetary value of the the search costs incurred by users of product search engines in a social media context. On average, searching an extra page on a search engine costs consumers \$39.15 and examining an additional offer within the same page has a cost of \$6.24, respectively. A good recommendation saves consumers, on average, \$9.38, whereas a bad one costs $18.54. Our policy experiment strongly supports this finding by showing that the quality of ranking can have significant impact on consumers’ search efforts, and customized ranking recommendations tend to polarize the distribution of consumer search intensity. Our model-fit comparison demonstrates that the dynamic search model provides the highest overall predictive power compared to the baseline static models. Our dynamic model indicates that consumers have lower price sensitivity than a static model would have predicted, implying that consumers pay a lot of attention to non-price factors during an online hotel search.
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