Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems

Peer-to-peer sharing platforms become increasingly important in the platform economy. From an HCI-perspective, this development is of high interest, as those platforms mediate between different users. Such mediation entails dealing with various social issues, e.g., building trust between peers online without any physical presence. Peer ratings have proven to be an important mechanism in this regard. At the same time, scoring via car telematics become more common for risk assessment by car insurances. Since user ratings face crucial problems such as fake or biased ratings, we conducted a design case study to determine whether algorithm-based scoring has the potential to improve trust-building in P2P-carsharing. We started with 16 problem-centered interviews to examine how people understand algorithm-based scoring, we co-designed an app with scored profiles, and finally evaluated it with 12 participants. Our findings show that scoring systems can support trust-building in P2P-carsharing and give insights how they should be designed.

Automated summary

Peer-to-peer sharing platforms are increasingly important in the platform economy and play a crucial role in mediating between users. Trust-building is crucial in such platforms, and algorithm-based scoring systems have the potential to improve this. To explore this, a design case study was conducted, involving interviews, app co-design, and evaluation with participants. The findings indicate that scoring systems can support trust-building in P2P-carsharing and provide insights on their design.