인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This study investigates how factors such as the number of properties, property rarity, and the number of trades influence NFT value, using path analysis within a structural equation modeling framework. Focusing on the Bored Ape Yacht Club (BAYC), a leading example of collectible NFTs, this analysis indicates that higher rarity and fewer trades generally correlate with greater NFT value. While some inconsistencies were observed in the relationship between rarity and the number of trades, an overall negative correlation emerged, suggesting that rarer NFTs are traded less frequently. The study also identifies an indirect effect of the number of trades on the relationship between rarity and NFT value, highlighting that both rarity and trading activity are valuable considerations when predicting NFT value trends. Interestingly, the number of properties exhibited varying impacts on value, emphasizing an area that warrants further exploration. Furthermore, findings confirm that the rarity of essential properties has a stronger influence on NFT value than additional properties, aligning with previous research. This highlights the importance of prioritizing essential property rarity when assessing NFT values. Collectively, these insights contribute to a deeper understanding of factors shaping NFT value and offer guidance for creators, investors, and collectors navigating the evolving NFT market.
본문·목차
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