It is now well understood that social media plays an increasingly important role in consumers’ decision making. However, an overload of social media content in product search engines can hinder consumers from efficiently seeking information. We propose a structural econometric model to understand consumers’ preferences and costs on search engines to improve user experience under unstructured social media. Our model combines an optimal stopping framework with an individual-level random utility choice model and analyzes click behavior in conjunction with purchase choices. Our model takes into accounts three major constraints in a consumer’s decision making process: (1) interdependency in decision making for different alternatives; (2) sequential arrival of information revealed by click-throughs; (3) non-negligible search cost. Our approach allows us to jointly estimate consumers’ heterogeneous preferences and search costs under the interplay of social media and search engines, and predict search and purchase behavior for each consumer. We validate the model using an individual session-level dataset of approximately 7 million observations resulting in room bookings in 2,117 U.S. hotels. Interestingly, our analysis allows us to quantify the trade-off between consumers’ benefits and cognitive costs from using large-scale unstructured social media information during decision making. Our policy experiments show that providing a carefully curated digest of social media content during the earlier stages of consumer search (i.e., on the search results summary page) can lead to a 12.01% increase in the overall search engine revenue.
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