Reverse double auction mechanism: An efficient algorithm for E-commerce platform operations

Qian Chen, Xuan Wang*, Cenying Yang, ZoeLin Jiang, Shuhan Qi, Jiajia Zhang, Na Li, Lei Wang, Jing Xiao

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Citations (Scopus)

Abstract

Transaction efficiency is critical to the success of e-commerce platforms. We propose a Reverse Double Auction Mechanism (RDouMech), an innovative auction-based algorithm, to revolutionize the transaction process in ecommerce operations. It incorporates unique features of reverse auction, multi-bids, and multi-constraints (i.e., market clearing, individual rationality, and incentive compatibility). This operational innovation facilitates market clearing by precisely matching buyers with sellers, leading to a balance between demand and supply, improved matching rates between buyers and sellers, and a significant increase in transaction prices on the platform. We further validate our algorithm using proprietary data from an online automobile transaction platform. Our results show that RDouMech raises the transaction revenues by 19.73% and doubles social welfare compared to the human expert-assisted artificial intelligence (AI) algorithm that is currently employed by the platform. © 2024 Published by Elsevier B.V.
Original languageEnglish
Article number101401
JournalElectronic Commerce Research and Applications
Volume65
Online published17 Apr 2024
DOIs
Publication statusPublished - May 2024

Research Keywords

  • Double auction
  • Business market
  • Incentive mechanism
  • Game theory

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