Trust is a fundamental prerequisite in the growth and sustainability of sharing economy platforms. Many of such platforms rely on actions that require trust to take place, such as entering a stranger’s car or sleeping at a stranger’s place. For this reason, understanding, measuring, and tracking trust can be of great benefit to such platforms, enabling them to identify trust behaviors, both online and offline, and identify groups which may benefit from trust-building interventions. In this work, we present the design and evaluation of a behavioral framework to measure a user’s propensity to trust others on Airbnb. We conducted an online experiment with 4,499 Airbnb users in the form of an investment game in order to capture users’ propensity to trust other users on Airbnb. Then, we used the experimental data to generate both explanatory and predictive models of trust propensity. Our contribution is a framework that can be used to measure trust propensity in sharing economy platforms via online and offline signals. We discuss which affordances need to be in place so that sharing economy platforms can get signals of trust, in addition to how such a framework can be used to inform design around trust in the short and long term.

The large majority of reputation systems use features such as star ratings and reviews to give users a reputation in online peer-to-peer markets. Both have been shown to be effective for signaling trustworthiness. However, the exact extent to which these features can change perceptions of users' trustworthiness remains an open question. Using data from an online experiment conducted on Airbnb users, we investigate which of the two types of reputation information --average star rating or the number of reviews --is more important for signaling a user's trustworthiness. We find that the relative effectiveness of ratings and reviews differ depending on whether reputation has a strong or a weak differentiation power. Our findings show that reputation effects are contingent on and susceptible to the context created by the alternative choices presented to users, highlighting how reputation information is displayed can drastically alter their efficacy for engendering trust.

ABSTRACT Building occupants perceive information about the surrounding environment and navigate for safety during emergency evacuation (egress). They often choose their actions by interacting with their social groups and observing the actions of the surrounding crowd. Most egress simulators, however, ignore individuals' crowd perceptions and social interactions. This paper presents a novel platform, SAFEgress (Social Agent For Egress), in which building occupants are modeled as agents who decide their evacuation actions on the basis of their knowledge of the building and their interactions with the social groups and the neighboring crowd. Results from the SAFEgress prototype show that both agents' familiarity with the building and social influence can significantly impact egress performance. By simulating different occupants' behaviors, architects and facility managers may better understand the influence of human and social factors on evacuation and consequently design safer buildings and egress procedures. Copyright © 2014 John Wiley & Sons, Ltd.