Enhanced digital embeddedness and bubble mitigation in NFT marketplaces: The impact of rarity rank on user trading behavior

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Decision Support Systems Pub Date : 2025-01-23 DOI:10.1016/j.dss.2025.114407
Lin Yuan , Chaoyue Gao , Alvin Chung Man Leung , Qiang Ye
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引用次数: 0

Abstract

As a nascent market in recent years, the NFT market has been widely scrutinized for its significant market bubble. To help investors make more informed trading decisions, several NFT marketplaces have introduced features that display the rarity information of NFTs directly on their interfaces. Existing literature on the rarity effect suggests that this feature generally increases trading activity. However, in the unique context of the NFT marketplace, its impact on user trading behavior remains an open question. This study focuses on the event where Rarible began displaying rarity information for profile picture (PFP) NFTs on its platform. Utilizing the theoretical perspective of dual process theory, we conceptualize the introduction of the rarity label as enhanced digital embeddedness. By using other NFT collections on the platform that have rarity information but do not display rarity rank labels as the control group, this study employs a rigorous Difference-in-Differences design. We find that this event leads to a decrease in both trading volume and trading price, primarily for lower-ranked NFTs, small-size collections, recent NFTs rather than top-ranked, large-size collections, established NFTs. Additional time-varying analysis also explains the asynchronous changes in price and trading volume. This study enriches the literature on the NFT marketplaces and the rarity effect, extends the application of dual process theory, and provides practical decision support for market regulators, managers, and platform users.
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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
期刊介绍: The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).
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