Building trust in online trade-in programs with a blockchain-enabled system
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, the authors 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. They then theoretically analyze the behavior of participants in the proposed system. In particular, they 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. They draw managerial and policy implications to improve system performance.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Chu, Xiang
- Wang, Rui
- Lin, Yan
- Li, Yantong
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0000-0002-9703-3882
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 102833
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Serial:
- Transportation Research Part E: Logistics and Transportation Review
- Volume: 164
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1366-5545
- Serial URL: http://www.sciencedirect.com/science/journal/13665545
Subject/Index Terms
- TRT Terms: Behavior; Blockchains; Electronic commerce; Feedback control; Fraud; Supply chain management
- Subject Areas: Administration and Management; Data and Information Technology; Freight Transportation;
Filing Info
- Accession Number: 01860046
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 30 2022 2:27PM