Incentivizing Data Quality in Blockchain-Based Systems—The Case of the Digital Cardossier
Spychiger, F., Tessone, C. J., Zavolokina, L., & Schwabe, G.
Inspired by an industry initiative to address the celebrated market for lemons (poor-quality used cars), we investigate how incentives for a permissioned blockchain-based system in the automobile ecosystem can be designed to ensure high-quality data storage and use by different stakeholders. The peer-to-peer distributed ledger platform connects organizations and car owners with disparate interests and hidden intentions. While previous literature has chiefly examined incentives for permissionless platforms, we leverage studies about crowdsensing applications to stimulate research on incentives in permissioned blockchains. This paper uses the action design research approach to create an incentive system featuring a rating mechanism influenced by data correction measures. Furthermore, we propose relying on certain institutions capable of assessing data generated within the system. This combined approach of a decentralized data correction and an institutionalized data assessment is distinct from similar incentive systems suggested by literature. By using an agent-based model with strategy evolution, we evaluate the proposed incentive system. Our findings indicate that a rating-based revenue distribution leads to markedly higher data quality in the system. Additionally, the incentive system reveals hidden information of the agents and alleviates agency problems, contributing to an understanding of incentive design in inter-organizational blockchain-based data platforms. Furthermore, we explore incentive