Mining Consumer Minds: Downstream Consequences of Host Motivations for Home-Sharing Platforms[Counting Your Customers’ One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model]

This research sheds light on consumer motivations for participating in the sharing economy and examines downstream consequences of the uncovered motivations. We use text-mining techniques to extract Airbnb hosts’ motivations from their responses to the question “why did you start hosting.” We find that hosts are driven not only by the monetary motivation “to earn cash” but also by intrinsic motivations such as “to share beauty” and “to meet people.” Using extensive transaction-level data, we find that hosts with intrinsic motivations post more property photos and write longer property descriptions, demonstrating greater engagement with the platform. Consequently, these hosts receive higher guest satisfaction ratings. Compared to hosts who want to earn cash, hosts motivated to meet people are more likely to keep hosting and to stay active on the platform, and hosts motivated to share beauty charge higher prices. As a result, these intrinsically motivated hosts have a higher customer lifetime value compared to those with a monetary motivation. We employ a multimethod approach including text mining, Bayesian latent attrition models, and lab experiments to derive these insights. Our research provides an easy-to-implement approach to uncovering consumer motivations in practice and highlights the consequential role of these motivations for firms.