Analysis of Influencing Factors on Collectibles NFT Value

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.

Authors

Publication Details

Journal
Korean Management Science Review
Published
2024-12-31
DOI
https://doi.org/10.7737/kmsr.2024.41.4.001
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Influencing Factors on Collectibles NFT Value

Geun-Cheol Lee, Hoon-Young Koo, Hee-Jung Lee
Korean Management Science Review
Industrial Vision Systems and Defect Detection
article

Analysis of Influencing Factors on Collectibles NFT Value

Geun-Cheol Lee, Hoon-Young Koo, Hee-Jung Lee
article en

Abstract

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.

Korean Management Science ReviewVol. 41(4)
Decent work and economic growth
Openalex Percentile: Top 61%
Industrial Vision Systems and Defect Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.