Abstract
Online trade-in programs provide efficient and convenient buyback services. However, they suffer from a trust deficit because of potential cheating by service providers (platforms). In this study, we design a blockchain-enabled system in which platforms tend to avoid cheating. The system employs an updated operation process to ensure reliable information inputs, a consortium blockchain network to avoid data tampering, and an intelligent algorithm embedded in a smart contract to automatically detect cheats. We then theoretically analyze the behavior of participants in the proposed system. In particular, we model the cheating decisions of a platform as chance-constrained programming and develop a Monte Carlo simulation method based on exploited optimality properties to solve it. Numerical experiments with real-world data demonstrate that the system reduces platforms’ motivation to cheat to an acceptable level. We draw managerial and policy implications to improve system performance.
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