Blockchain technology is an innovation of the 21 st century that is supposed to act as a trust-building factor and may provide the foundation for trust-free systems as well as market exchanges. We investigate how the trustrelated properties of blockchain technology influence trust relationships of participants in the platform economy. Building on the pilot study of Hawlitschek [1], we conduct a scenario-based online survey with participants taking the role of a customer on a blockchain-based peer-to-peer rental platform. Our results confirm that while trust in peers and shared products have no overall significant effect on rental intentions, trust in blockchain technology and the community of blockchain users drive rental intentions mediated by trust in the blockchain-based platform. Our study sheds light on how established trust relationships shift from a peer and product focus towards trust in platforms and their underlying technology.

Bitcoin, as the most popular cryptocurrency, has received increasing attention from both investors and researchers over recent years. One emerging branch of the research on bitcoin focuses on empirical bitcoin pricing. Machine learning methods are well suited for predictive problems, and researchers frequently apply these methods to predict bitcoin prices and returns. In this study, we analyze the existing body of literature on empirical bitcoin pricing via machine learning and structure it according to four different concepts. We show that research on this topic is highly diverse and that the results of several studies can only be compared to a limited extent. We further derive guidelines for future publications in the field to ensure a sufficient level of transparency and reproducibility.