A computational framework for understanding antecedents of guests' perceived trust towards hosts on Airbnb

Abstract

Several studies have researched the antecedents influencing the perceived trust of guests towards hosts on Airbnb typically relying on survey data. However, the contribution of these antecedents to trust building in a practical context remains unclear. To fill this gap, we focused on the antecedents within the manageable information about hosts and proposed a computational framework for understanding the antecedents influencing perceived trust. Specifically, perceived trust was proxied by the growth rate of bookings and the validity of the proxy method was proved through comparing with human labeled data. From the snapshot information about hosts, the antecedents were quantified through text mining and face recognition methods. The least square regression was applied to analyze and compare the influence of these antecedents. We found that the contribution of reputation is not less than the summation of all the other antecedents. Additionally, in terms of self-descriptions, it is worthwhile to pay more attention to interactions and services. Expressing positive sentiment in either self-descriptions or profile photo is also helpful. The response behavior pattern and the number of verifications also matter. At last, several effective trust prediction models were built by using deep neural network and the ensemble method. The findings shed light on the working of the antecedents in trust formation and can provide instructions for the transaction partners, designers and managers of online services in the sharing economy.