• ISSN: 2301-3567 (Print), 2972-3981 (Online)
    • Abbreviated Title: J. Econ. Bus. Manag.
    • Frequency: Quarterly
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Fiona Chu
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HOME > Archive > 2025 > Volume 13 Number 3 (2025) >
JOEBM 2025 Vol.13(3): 315-319
DOI: 10.18178/joebm.2025.13.3.885

B2G Auctions and the Classical Law of Supply and Demand: An XAI-Enabled Deep Learning Approach

Victor Kwan* and Chong Guan
Singapore University of Social Sciences, S599494, Singapore
Email: victorkwanyh@suss.edu.sg (V.K.); guanchong@suss.edu.sg (C.G.)
*Corresponding author

Manuscript received January 25, 2025; accepted May 2, 2025; published September 19, 2025.

Abstract—This study explores the rationality of B2G auctions through Singapore’s Certificate of Entitlement (COE) system, where car ownership is regulated via online auctions. Utilizing 20 years of data, a XAI-enabled deep learning approach reveals the influence of supply and historical prices on bidding outcomes. These findings align B2G auctions with classical economic theories, which is unlike irrational auction behaviors found in C2C and B2C contexts. This research contributes to auction theory and offers policy makers applicable insights into pricing strategies.

Keywords—auction theory, B2G auctions, deep learning approach, pricing strategies, rational auction behaviors

Cite: Victor Kwan and Chong Guan, "B2G Auctions and the Classical Law of Supply and Demand: An XAI-Enabled Deep Learning Approach," Journal of Economics, Business and Management, vol. 13, no. 3, pp. 315-319, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

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