Abstract
Purpose Airbnb Experiences is a new type of service launched by Airbnb in November 2016, where users can offer travellers a wide range of activities. This study devotes attention to analysing customer feedback expressed in online reviews published in Airbnb to evaluate those experiences. Design/methodology/approach A total of 1,110 reviews were collected from 12 categories, including 111 experiences, resulting in 10 reviews per experience. First, the sentiment score was computed based on the text of the reviews. Second, 17 quantitative features encompassing user, Airbnb experience and review information were used to model the score through a support vector machine. Third, a sensitivity analysis was performed to extract knowledge on the most relevant features influencing the sentiment score. Findings Tourists writing online reviews are not only influenced by their tourist experience but also by their own online experience with the booking and online review platform. The number of reviews made by the user accounted for more than 20 per cent of relevance, while users with more reviews tended to grant more positive reviews. Originality/value Current literature is enhanced with a conceptual model grounded on existing studies that assess tourist satisfaction with tour services. Both services online visibility and user characteristics have shown significant importance to tourist satisfaction, adding to the existing body of knowledge.
Highlights
The innovative Internet-based environment has been a stepping-stone to new types of businesses in the tourism industry
The present study is focused on modelling the sentiment score from the textual reviews of Airbnb Experiences offered throughout the globe using the remaining features that characterise the experience and the tourist who wrote the review
The support vector machine (SVM) model’s performance was assessed by two metrics: the mean absolute error (MAE), which measures the average deviation of the score computed by the model against the real score; and the normalised area under the regression error characteristic curve (NAREC), which measures the error tolerance versus the percentage of points predicted within the tolerance, with 0.5 representing a random guess model, and 1.0 a perfect model (Hyndman et al, 2016; Huntsinger, 2017)
Summary
The innovative Internet-based environment has been a stepping-stone to new types of businesses in the tourism industry. Airbnb’s expansion strategy beyond lodging has led them to offer a new service they named “Experiences” based on immersive travel experiences such as gastronomy tours and nature activities (Kokalitcheva, 2016; Meltzer, 2016). While this is a recent service, it benefits from the general features of the Airbnb platform, namely the mandatory online reviews system where users can provide valuable feedback on the booked services. We aim to unfold tourist feedback into its main available dimensions in the post-purchase experience based on online reviews. We expect the diversity of services provided through Airbnb and the large number of online reviews available to provide additional insights to existing literature
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