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Combating fraudulent returns using blockchain technology

delete2023-04-27
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PRE
AI
Z
Zhao Liu
X
Xiaoqiang Cai
W
Wenju Niu *
L
Lianmin Zhang
DOI:10.1111/itor.13306delete
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Abstract

Abstract

En 中文
Fraudulent returns have become increasingly frequent in the development of online retail platforms, leading to huge losses for retailers. A blockchain-based system can help eliminate this problem primarily because the data stored in a blockchain network cannot be modified. In this paper, we study the impact of blockchain technology that aims to eliminate fraudulent return behavior on retailers and their upstream suppliers. We develop a game-theoretic model that comprises a supplier, a retailer, and customers and analyze the equilibrium outcomes. The results show that the retailer would (not) adopt blockchain technology if the efficiency of the supplier's investment in the innovation of its products is high (low). Moreover, if the retailer's loss from the return is low (high), the adoption of blockchain technology decreases (increases) the supplier's investment and the wholesale and retail prices. In terms of its effect on profitability, we reveal that if the fraudulent return losses are high, using blockchain technology always benefits the supplier; otherwise, it increases retailer profitability and supply chain performance but may make the supplier suffer losses in profits. Interestingly, the retailer would adopt blockchain technology to ensure profitability in situations where the number of customers without fraudulent returns and the intensity of losses are low; however, this weakens supplier profitability and meanwhile reduces supply chain performance.
Keywords:
blockchain technology
fraudulent returns
innovation investment
supply chain management

Journal

International Transactions in Operational Research cover
International Transactions in Operational Research
IF:
2.9
Papers:
1.8K
Citations:
3.7K

Organization

T
The Chinese University of Hong Kong, Shenzhen
Scholars:
4.3K
Papers: 4.0K
Citations: 7
S
Shenzhen Research Institute of Big Data
Scholars:
256
Papers: 352
Citations: 357
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