Abstract

Our research aims to address the following research questions: (a) to identify guests' hidden experiences in a distribution of terms over a fixed vocabulary by analysing a bulk set of online reviews through the process of text mining, and in particular, (b) to assess if the Airbnb guest experience represented in them can be used to enhance Airbnb services. On the other hand, our study analyses the relationship between the topics identified and Airbnb pricing, and mainly measures the influence of gender as a moderating cue. In this regard, a growing body of research has emerged to examine gender differences in leisure participation. In particular, our study concludes how the guests' gender affects the contributions of listings' features in price prediction. Females are more intrinsically motivated and preferentially mention, for instance, the Airbnb accommodation's location and the gratifying (local) experiences in their narratives. On the contrary, male guests highlight hygiene and apartment facilities. To sum up, our research provides design guidelines to reflect the willingness to hire an apartment, offering insights for research and practice, and allowing the layout of pricing-recommendation systems.