Main article

Nadia A. Rahman
Faculty of Business and Management, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
Harith M. Johan
Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia
Mei Lin Tan*
School of Business Innovation and Technopreneurship, Universiti Malaysia Perlis, Kangar 01000, Malaysia
meilintan@unimap.edu.my

DOI: https://doi.org/10.63646/jbda.2023.010302

Abstract

Non-fungible token (NFT) platforms increasingly support assets whose transferability is shaped by vesting periods, staking commitments, royalty duties, access rights, and collection-specific transfer rules. These contractual features make NFT exchange more complex than simple peer-to-peer barter because an allocation that improves asset fit may still violate term consistency, expose users to hidden obligations, or reduce platform-level trust. This article develops a data-driven mechanism design framework for contract-constrained NFT exchange platforms. The framework integrates smart-contract metadata, wallet-level trading histories, inferred preference rankings, and transaction-risk indicators into a contract-aware matching process. Instead of treating NFTs only as indivisible goods, the study models each exchange option as a digital asset bundled with a transfer term. A data-driven equal-term top trading cycles mechanism is then proposed to balance allocative efficiency, individual rationality, term consistency, and manipulation resistance. Numerical experiments are conducted using a synthetic platform dataset calibrated to realistic NFT-market features, including heterogeneous user preferences, different shares of locked assets, royalty-bearing transfers, and varying degrees of preference noise. The results show that a standard top trading cycles rule generates high apparent efficiency but creates frequent term-consistency violations when restricted assets are common. A contract-filtered rule eliminates violations but loses welfare by blocking too many mutually beneficial exchanges. The proposed data-driven equal-term mechanism achieves the strongest overall platform score by combining constraint screening with preference learning and risk-sensitive tie breaking. The findings contribute to business data analytics, digital platform governance, and market design by showing how data infrastructure can translate programmable property rights into operational exchange rules.

Article details

How to Cite

Rahman, N. A., Johan, H. M., & Tan, M. L. (2023). Data-Driven Mechanism Design for Contract-Constrained NFT Exchange Platforms. Journal of Business and Data Analytics, 1(3), 23-45. https://doi.org/10.63646/jbda.2023.010302