The Role of Trust Indicators in a Digital Platform: A Differentiated Goods Approach in an Airbnb Market

Abstract

We employed a differentiated goods market analysis where we utilized, in addition to the prevailing goods’ attributes, different types of the sellers’ attributes including trust indicators. We incorporated both sets of attributes into a structural model of the whole market including demand and pricing equations with the hotels as an outside good. Applying the model to Stockholm’s Airbnb market reveals that hosts’ attributes significantly affect market performance. Simulations of market scenarios show that the very high review scores and the Superhost certification help Airbnb compete with hotels and increase profitability. We also show that the “Airbnb plus” luxury program, which makes the apartments a closer substitute for hotels, leads to an increase in Airbnb’s market welfare. Our analysis provides a framework for understanding the full impact of the different attributes of the products, their providers, and their management in the sharing-economy accommodation market.

The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment

“…In terms of price discrimination, Aznar, Sayeras, Segarra, and Claveria (2018) conclude that Airbnb hosts in Barcelona discriminate prices according to seasonality, but generally not in terms of day of the week. Although host attributes are important price determinants ( Wang & Nicolau, 2017 ) and act as trust indicators ( Fleischer et al, 2022 ), the recent contribution by Barnes (2021) warns about the overvaluation of perceived facial trustworthiness in Airbnb host images, especially in combination with reputational measures.…”

“…Ignoring the endogeneity of Airbnb demand as a determinant of Airbnb price when finding coefficient estimates through standard ordinary least squares (OLS) estimation is likely to introduce systematic bias. Hence, Airbnb demand needs to be properly instrumented to avoid such bias ( Wooldridge, 2010 ), yet this has not been regularly applied in Airbnb pricing and demand research to date: the recent contribution by Fleischer, Ert, and Bar-Nahum (2022) representing a notable exception.…”

How do Different Types of User-Generated Content Attract Travelers? Taking Story and Review on Airbnb as the Example

“…With regard to the control variables, existing studies have explored two important factors influencing reservations on Airbnb, including the characteristics of the properties and services, by recalling the review part of this work. In accordance with previous research, this study controls the characteristics of the property through four variables: price (P) (Fleischer et al, 2022), accommodation type (T) (Yang & Mao, 2019), size (S) (Gunter et al, 2020), and cleaning fee (CF) (Dogru et al, 2021). In terms of services, two variables are controlled, including minimum nights (MD) (Gunter et al, 2020) and Superhost title (SH) (Liang et al, 2017).…”

How Do Voice Characteristics Affect Tourism Interpretation Purchases? An Empirical Study Based on Voice Mining

“…In tourism, digital platforms mainly include online booking platforms (e.g., Booking.com, Airbnb) and online travel agencies (Bilbil, 2019). Although different platforms operate in different markets, they are changing tourism economics (Fleischer et al, 2022). Tourism studies have studied different digital platforms, such as peer-to-peer platforms (Fleischer et al, 2022), online booking platforms (Bilbil, 2019), online review platforms (Mehraliyev et al, 2021), travel writing platforms (Salet, 2021), and cultural heritage online dissemination platforms (Pesce et al, 2019).…”

“…Digital platforms have become one of the most prominent forms of digitalization and hence drivers of tourism development (Fleischer et al, 2022; Gössling, 2021; Mehraliyev et al, 2021). This trend is also visible in tourism interpretation.…”