Trust and Reputation in the Sharing Economy on the Example of the Airbnb Platform

This work is devoted to studying the role of trust and reputation in the sharing economy using the example of the Airbnb platform. The main object of this study is to determine the factors that influence consumer confidence in the Airbnb online service, to identify their significance, and to formulate general recommendations for increasing the trust of users of this platform. To achieve this goal, the following tasks were completed: 1. Collection, analysis and systematization of the existing theoretical base on the topic of trust and reputation in the economy of joint consumption; 2. Conducting a quality content analysis of negative consumer reviews both on the Airbnb platform itself and on third-party sites; 3. The formation of the aggregate of the most common factors affecting consumer confidence; 4. Development of a model of consumer confidence in the landlord, as the ultimate service provider, and on the Airbnb platform, as an intermediary; 5. Conducting an online survey of users of the Airbnb platform and subsequent interpretation of the data obtained to identify the significance of confidence factors; 6. Formation of general recommendations for lessors and platforms aimed at increasing consumer confidence. In the framework of this work, an extensive and qualitative analysis of the relevant theoretical base was carried out, with the help of which a holistic representation of the studied phenomenon of trust in the conditions of the sharing economy was formed. Further, using the Airbnb online platform as an example, using a content analysis of negative reviews, specific factors affecting consumer confidence in landlords as final service providers and the online platform as an intermediary were investigated and classified. The result of this process was a model of aggregate trust, which contains all 14 identified determinants. The formulated factors were evaluated in terms of their significance. Confidence factors for the supplier were analyzed using descriptive statistics based on the results of the survey, while the factors affecting the credibility of the intermediary platform were analyzed based on regression analysis, which made it possible to identify the relationship between these determinants and the general level of user confidence in the platform, based on their experience of interacting with the service.