Is It Wise to Adopt Blockchain Technology to Cope With the Unauthorized Domestic Gray Markets?

块链 灰色(单位) 计算机安全 计算机科学 业务 灰色市场 产业组织 技术管理 分布式账本 人工智能
作者
Shipra Shukla,Abhishek Srivastava,Aarushi Mahajan,Nitin Koshta
出处
期刊:IEEE Transactions on Engineering Management [Institute of Electrical and Electronics Engineers]
卷期号:: 1-18
标识
DOI:10.1109/tem.2026.3726490
摘要

The unauthorized selling of genuine products in gray markets is a growing concern for companies worldwide. Gray markets create unfair competition by offering genuine products at different prices, which cannibalizes demand from authorized channels. Consumers often unknowingly purchase products from unauthorized channels and feel deceived when they discover that these products are unauthorized and often lack after-sales services and warranty benefits. This negatively impacts the brand image and forces the manufacturer to lower product prices to compete with the gray market, ultimately reducing the manufacturer’s profit. We address this issue by analyzing how brands and manufacturers can use blockchain technology to combat gray markets. Surprisingly, we find that despite a reduction in total channel sales, blockchain implementation is an optimal strategy for the brand/manufacturers when both the product’s brand equity and the proportion of myopic consumers in the market are relatively high. Under blockchain technology (BT) implementation, the retailer incurs losses when the proportion of myopic consumers is moderate to high, due to reduced product diversion to the gray market and a decrease in total channel sales. In addition, we discuss the implications of blockchain adoption on consumer surplus and social welfare. Furthermore, we extend the model to explore the impact of markdown product selling, retailer’s auditing policy, service competetion, uncertain demand, and variable blockchain implementation costs on adoption decisions. We find that the authorized retailer benefits from blockchain implementation when markdown products are involved, particularly when waiting costs and channel differentiation are low. Finally, we demonstrate the robustness of our results through several practically relevant model extensions.
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