Reputation offsets trust judgments based on social biases among Airbnb users

Abstract

To provide social exchange on a global level, sharing-economy companies leverage interpersonal trust between their members on a scale unimaginable even a few years ago. A challenge to this mission is the presence of social biases among a large heterogeneous and independent population of users, a factor that hinders the growth of these services. We investigate whether and to what extent a sharing-economy platform can design artificially engineered features, such as reputation systems, to override people's natural tendency to base judgments of trustworthiness on social biases. We focus on the common tendency to trust others who are similar (i.e., homophily) as a source of bias. We test this argument through an online experiment with 8,906 users of Airbnb, a leading hospitality company in the sharing economy. The experiment is based on an interpersonal investment game, in which we vary the characteristics of recipients to study trust through the interplay between homophily and reputation. Our findings show that reputation systems can significantly increase the trust between dissimilar users and that risk aversion has an inverse relationship with trust given high reputation. We also present evidence that our experimental findings are confirmed by analyses of 1 million actual hospitality interactions among users of Airbnb.

Keywords: online trust; reputation systems; risk; sharing economy; social biases.

Fig. S1.

Partial view of the screen the participant sees during the experiment. It shows the participant’s profile against one of the synthetic profiles.

Fig. S2.

Example of the structure of a user session. The symbol S in the figure indicates the same values as the participant’s features; D indicates a different value, which increases distance in the social space; and B indicates baseline reputation. The random decision of which feature to vary to increase distance from $d=0$ to $d=1$ is labeled $R1$, and to $d=2$, $R2$, and the reputation feature we vary, $R3$. Other random choices include the profiles’ age and region (whenever they have to be different from those of the player) to values outside of the player’s own age group and region.

Fig. 1.

Empty model estimates of average investment in profile at distance $d$ and average savings. (A) In world 1, the second profile at distance $d=4$ (here identified as $d=5$) has a worse reputation than baseline. (B) In world 2, the profile at distance $d=5$ has a better reputation than the baseline.

Fig. 2.

The effects of the covariates associated with the participant (S) and profiles (P) in the multivariate multilevel model. The dashed lines have the values $\pm 1.37$, which correspond to the smallest average investment difference between two profiles with baseline reputation, minus two standard errors.

Fig. 3.

Real-world data from Airbnb show that an increased reputation of the host in the form of rating (graph) and number of reviews (x axis) results in greater diversity of guests who selected them (y axis).