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.

    Highlights Achieve customers’ trust during the process of online acquisition. Design a blockchain-enabled online trade-in system for used electronics. Propose an intelligent algorithm to detect collusive behavior with the support of a blockchain system. Provide managerial insights to maintain a trustworthy online trade-in environment.


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    Titel :

    Building trust in online trade-in programs with a blockchain-enabled system


    Beteiligte:
    Chu, Xiang (Autor:in) / Wang, Rui (Autor:in) / Lin, Yan (Autor:in) / Li, Yantong (Autor:in)


    Erscheinungsdatum :

    2022-07-08




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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