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.
Highlights
Overall, recommendation systems are based on a sharing economy by connecting supply and demand through peerto-peer platforms, and in particular, modify the way in which travellers contract hospitality services
Each SHAP value represents a descriptive approximation of the predictive model and describes how much each topic in our model contributes, either positively or negatively, to increasing the price-levels of Airbnb accommodations
Having a high proportion of terms assigned to topic 6 is associated with high and positive values on the target (Airbnb price)
Summary
The linguistic attributes of natural and non-structured UGC (associated with a utilitarian, enjoyable, social and home-like accommodation experience) are still largely under-explored in the home-sharing literature (Zhao et al, 2019), and in their influence on pricing decisions (cf Sanchez-Franco, Troyano-Jimenez, et al, 2019). Our study is relevant for a major understanding of the associations between gender and prices that would enable hosts to be more effective in setting appropriate pricing policies and would be a relevant key to differentiate the touristic rentals. These questions are analysed in an ambitious study of natural and non-structured UGC – extracted from Airbnb – that identifies users’ experience-related topics. The discussion section outlines the future lines of research and theoretical and managerial implications
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