Herding behavior in NFT Auction: The role of visual complexity and familiarity

Abstract

Despite the growing popularity of non-fungible tokens (NFTs) in the market, they still lack established valuation guidelines. Invoking herding and auction theories, this study investigates how NFT collectors’ bidding tendencies are influenced by other collectors’ bidding decisions (herding behavior). This study also examines how NFT visual characteristics, such as complexity and familiarity, moderate herding behavior. We use a computer vision technique to quantify the visual complexity of an NFT and apply transfer learning to capture its visual familiarity. Examining data from a widely used NFT platform, we find evidence of herding behavior in such auctions. Furthermore, our results show that there are nonlinear moderating effects of visual characteristics on herding behavior. Specifically, we find that consumers exhibit a stronger herding tendency at the two extremes of complexity distribution and at the lower extreme of familiarity distribution. Further analysis of the NFT resale market suggests that auction winners consider herding in the primary sale of an NFT as a signal of high demand; thus, they are more willing to resell it and set a higher price premium in the resale. These findings can be taken as input by the NFT market platforms in their NFT display decisions to boost primary market sales and promote resale market participation. The findings can also help content creators formulate creative strategies that lead to higher bidding probability or sales performance.