Dustin Weiss, Robert Gaudiosi, Z. Ivy Zhou, Robert I. Webb
This paper examines intraday Bitcoin spot returns and trading activity around the expiration of Deribit Bitcoin options. Using data from spot exchanges and Deribit perpetual futures, we document a statistically and economically significant return reversal around expiration. The effect concentrates on days with elevated at-the-money open interest and is strongest when cumulative gamma exposure is negative, which is consistent with positive feedback trading pressure induced by option market makers hedging net short exposure. Trading activity also rises around expiry in Deribit perpetual futures and in the spot exchanges used to determine the Deribit settlement price. These intraday price effects are economically meaningful, implying annual wealth transfers of approximately USD 50 million between option writers and holders. Overall, the findings highlight the role of daily option expirations in shaping short-horizon price formation in Bitcoin markets and have implications for regulated investment products that rely on spot-market reference prices.
Since 2021, interest in non-fungible tokens (NFTs) and associated trading volume have increased substantially, as celebrities increasingly adopted profile picture non-fungible tokens (PFP NFTs) for their social media profile images. In this study, the factors influencing consumer decisions on purchasing a PFP NFT were analyzed by Conjoint analysis. The characteristics of profile picture and NFT were researched through previous studies, and key attributes and levels that affect purchasing of a PFP NFT were set through market research. The results of the study showed that consumers made decisions based on the number of promoting celebrities as the most important attribute when they buy a PFP NFT, followed by number of community members, floor price, and commercial use of NFT intellectual property. This research has value in that it suggests a forward-looking perspective regarding development of the NFT market, which is in its early stages.
Open access
Consumer Market Behavior and Pricing
Consumer Behavior in Brand Consumption and Identification
Do online narratives leave a measurable imprint on prices in markets for digital or cultural goods? This paper evaluates how community attention and sentiment relate to valuation in major Ethereum NFT collections after accounting for time effects, market-wide conditions, and persistent visual heterogeneity. Transaction data for large generative collections are merged with Reddit-based discourse measures available for 25 collections, covering 87{,}696 secondary-market sales from January 2021 through March 2025. Visual differences are absorbed by a transparent, within-collection standardized index built from explicit image traits and aggregated via PCA. Discourse is summarized at the collection-by-bin level using discussion intensity and lexicon-based tone measures, with smoothing to reduce noise when text volume is sparse. A mixed-effects specification with a Mundlak within--between decomposition separates persistent cross-collection differences from within-collection fluctuations. Valuations align most strongly with sustained collection-level attention and sentiment environments; within collections, short-horizon negativity is consistently associated with higher prices, and attention is most informative when measured as cumulative engagement over multiple prior windows.
Open access
3 source records
econ.GN
Consumer Behavior in Brand Consumption and Identification
Mohsen Minaei, Ranjit Kumaresan, Andrew Beams, Pedro Moreno-Sånchez ¡ 9 authors
Blockchain auction plays an important role in the price discovery of digital assets (e.g.NFTs).However, despite their importance, implementing auctions directly on blockchains such as Ethereum incurs scalability issues.In particular, the on-chain transactions scale poorly with the number of bidders, leading to network congestion, increased transaction fees, and slower transaction confirmation time.This lack of scalability significantly hampers the ability of the system to handle largescale, high-speed auctions that are common in today's economy.In this work, we build a protocol where an auctioneer can conduct sealed bid auctions that run entirely off-chain when parties behave honestly, and in the event that k bidders deviate (e.g., do not open their sealed bid) from an n-party auction protocol, then the on-chain complexity is only O(k).This improves over existing solutions that require O(n) on-chain complexity, even if a single bidder deviates from the protocol.In the event of a malicious auctioneer, our protocol still guarantees that the auction will successfully terminate.We implement our protocol and show that it offers significant efficiency improvements compared to existing on-chain solutions.Our use of zkSnark to achieve scalability also ensures that the on-chain contract and other participants do not learn anything about the bidders' identities and their respective bids, except for the winner and the winning bid amount.
Non-fungible token (NFT) marketplaces are the main venues for NFT transactions. In recent years, these platforms have introduced âSweeping,â a bulk purchasing feature intended to improve the buying experience and enhance market liquidity. Although this feature offers clear benefits, such as simplifying the purchase of multiple NFTs and reducing gas fees, its actual effects on market dynamics remain underexplored. This study examines how the bulk purchasing feature affects two key dimensions of the NFT market, liquidity and speculation. Using a comprehensive dataset of Ethereum on-chain NFT transactions, NFT collection characteristics, and Twitter data, we adopt a rigorous identification strategy that combines Propensity Score Matching (PSM) with Difference-in-Differences (DID) estimation to identify causal effects. We find that the bulk purchasing feature significantly increases both liquidity (measured by transaction volume and sales count) and speculation (measured by price volatility and turnover rate) at the collection level. These results are consistent with Transaction Cost Economics (TCE). We also find heterogeneous effects. Collections with higher economic and social value experience larger gains in liquidity. In contrast, the effect on speculation remains similar across collections with different value attributes. These findings offer decision support for NFT marketplace operators seeking to design and implement bulk trading mechanisms.
Non-fungible tokens (NFTs) present luxury brands with a pricing dilemma: high prices sustain quality inferences but invite visible failure on transparent blockchain markets, whereas low prices stimulate demand but anchor perceptions downward. This research investigates zero pricing (free distribution) as a strategy to navigate this dilemma. Analysis of 65 NFT collections from 32 brands on OpenSea and 22,841 posts on X is followed by six experiments (N = 1,924). Low-priced NFTs inflict the most severe loss of brand luxuriousness, yet free NFTs attenuate this loss to levels indistinguishable from comparable pricing (Study 1). This attenuation does not extend to physical products, implicating congruence between inferred cost structure and zero pricing as a governing condition (Study 2). When secondary-market demand declines, free NFTs weaken failure inferences that otherwise erode perceived luxury; however, this buffer dissipates when the NFT features flagship brand elements (Studies 3a-3c). When demand increases, free and paid NFTs yield equivalent recovery (Study 4). Free distribution thus caps downside risk without forfeiting upside potential. These findings advance the zero-price literature by establishing inferred cost structure as a boundary condition for the anchor-shift mechanism and equip brand managers with a pricing approach calibrated to the transparency of blockchain-based markets.
Open access
Consumer Behavior in Brand Consumption and Identification
Rarity is a key determinant of value in non-fungible token markets, yet its measurement remains fragmented, opaque, and theoretically underdeveloped. We analyse the statistical and combinatorial rarity metrics used by major platforms and show that most reduce to transformations of the Pythagorean means. The widely-used OpenRarity metric produces rankings identical to the geometric mean of attribute frequencies and is therefore not a new methodology. However, this approach admits a coherent probabilistic interpretation only under the assumption of trait independence. Alternative arithmetic- and harmonic-mean metrics lack theoretical justification, while the Jaccard distance is structurally biased when metadata omit missing traits. When metadata are standardised appropriately, Jaccard-based rankings are identical to the arithmetic mean ranks. These findings explain pervasive inconsistencies in rarity rankings and help explain the weak empirical relationship between price and rarity. We propose a standardised, theoretically grounded framework for rarity measurement that accommodates dependence structures and corrects metadata bias.
This research explores the application of data mining techniques, specifically XGBoost, to predict game pricing trends and optimize discount strategies within the digital gaming market. Game prices are influenced by various factors, including production costs, market demand, and promotional strategies. This study analyzes historical pricing data from multiple online stores to identify key pricing patterns and factors that influence price changes over time. The model developed in this study predicts game prices by incorporating features such as retail price, discount percentages, past price trends (lags), and other time-based features. The findings reveal that retail price and recent price trends (e.g., 7-day rolling averages) are the most influential features in predicting future prices. Additionally, discount strategies significantly impact game sales, with certain discount ranges showing higher effectiveness in driving consumer purchases. The model also demonstrates variability in prediction accuracy, particularly at higher price points, highlighting the challenges of capturing complex price fluctuations in a dynamic digital marketplace. The significance of this study extends to the Metaverse market, where pricing and the use of digital assets like non-fungible tokens (NFTs) play a critical role. The model's application could aid in optimizing pricing strategies within virtual economies, enhancing both the consumer experience and retailer profitability. Future work includes integrating additional features such as user reviews and exploring its application to Metaverse game platforms. The practical implications of this research are significant for online game retailers looking to leverage data-driven insights for more effective pricing and promotional strategies.
Introduction This study explores the feasibility of embedding futarchy, specifically policy-binding conditional prediction markets anchored to democratically chosen key performance indicators (KPIs) in Decentralized Science (DeSci) governance. By externalizing belief formation to speculative markets while anchoring values democratically, futarchy offers a structurally distinct alternative to existing Decentralized Autonomous Organization (DAO) governance models. Methods Through an empirical analysis of governance data from 13 DeSci DAOs, this study examines governance, participation, and cadence patterns that condition futarchic adoption. A retrospective simulation using proposals from VitaDAO assessed the degree to which historical decisions align with futarchy-preferred outcomes. Results The results indicate full directional alignment under deterministic modeling, suggesting latent compatibility between futarchy and existing DeSci governance. Discussion The analysis further outlines the design principles for implementation, emphasizing measurable KPIs and epistemic diversity. Futarchy, if carefully instantiated, may serve as a governance alternative for funding truth-tracking science through probabilistic decision making and market-based information aggregation.
Majd AbedRabbo, Zeina AlMalak, Fiona EllisâChadwick, Jοãο S. Oliveira
ABSTRACT This paper explores consumers' drivers and motivations behind luxuryâfashion nonâfungible tokens (NFTs) ownership and the implications of the potential ownership of these NFTs on the purchase intentions of physical luxury products of the same brand. Hitherto, little research has been conducted on the consumer's perception of ownership and its effect on physical product purchases. Following the Self Determination Theory (SDT), a twoâstep qualitative research approach is implemented due to the lack of empirical research in this area. This study focuses on luxury fashion NFTs and targets millennials and generation Z consumers. A total of 4 focus groups (25 participants) and 6 semiâstructured interviews were conducted to address the objectives of this research. Using thematic analysis, the study identifies 5 key drivers behind NFTs ownership: authenticity, exclusivity, scalability, affordability, and digital literacy. Scalability of luxury fashion NFTs valuation is found to be a critical driver of consumers' ownership intentions. Similarly, digital literacy was identified as a new driver of intentions of ownership of luxury NFTs considering its effect on consumers' social status. Finally, depending on consumers' lifestyle, ownership of luxury fashion NFTs is argued to have a mixed effect on the intentions of ownership of physical luxury products. This research contributes to the development of the understanding of the emerging concept of luxury NFTs and their profound influence on consumers' perceptions of ownership and purchase intentions for physical luxury products.
Open access
Consumer Behavior in Brand Consumption and Identification
ABSTRACT Using a textâbased measure of peer opinions constructed from cryptocurrencyârelated social media posts, we find that peer opinions contain valuable information about the prices of cryptocurrency options. Bitcoin options exhibit a volatility smile, which becomes steeper when peer opinions become bearish. The riskâneutral skewness of Bitcoin returns implied by options prices becomes more negative in times of bearish opinions. The predictability of peer opinions for Bitcoin option prices remains robust after controlling for momentum, volatility, demand pressures, news effects, and other sentiment measures, and exhibits no evidence of reversal over time. This effect is pronounced when Bitcoin attracts high investor attention, more diverse opinions about Bitcoin are expressed on social media, and Bitcoin options are more actively traded. We find similar results for Ethereum options.
This study examines how firms in the PokĂŠmon Trading Card Game (PTCG) grading industry adapt their business models in response to digital disruption. We employ a qualitative multiple-case design, investigating three leading grading companies â PSA (United States), CCIC (China), and SQC (Thailand) â through 30 in-depth interviews and supplemental document analysis. The findings reveal divergent strategies shaped by both dynamic capabilities and institutional contexts. PSA leverages scale and AI technology to enhance efficiency, CCIC focuses on legitimacy and incremental improvements under regulatory constraints, and SQC pursues exploratory digital initiatives (e.g., NFT-linked trials) to co- create value with its community. These patterns highlight the ambidexterity required for business model innovation in a digitizing niche service sector. The study contributes to business model innovation and digital transformation literature by demonstrating how national institutions and customer engagement influence innovation paths. Practical implications include lessons for balancing core business sustainability with transformative innovation in different regulatory environments.
Michael Smith, Valerie Kilders, Todd Kuethe, Nicole Olynk Widmar
We examine the relationship between market performance of leading cryptocurrencies (Bitcoin and Ethereum), meme-stocks (AMC, GameStop), and subjects of corporate boycotts (Bud Light) using weekly market price and volume data along with social media data of weekly mentions (which total 337 million in this dataset) and net sentiment. Using vector autoregression (VAR) time series analysis along with Granger causality testing and structural breaks, we successfully predict trade volume of these various assets using social media data and price data. We also find that closing price data and trade volume are reliable predictors of net sentiment about crypto in online and social media. However, we struggle to predict the closing price for the group of assets studied. We also employ impulse response functions, finding evidence of a dynamic relationship occurring between online and social media net sentiment and online media volume with closing price and trade volume. These functions show that investor sentiment operates with a short memory lasting around 3 weeks, additionally these functions show that price generates a shock on trade volume but that crypto and meme-stock markets experience this differently. Our findings reinforce the notion that meme-stock traders and herd investors do not trade on market fundamentals but are instead sensitive to herding (or sentiment) movements. Our findings also suggest that compared to these meme-stock investors, crypto markets have more traditional motivations of loss aversion.
In online auctions, fraudulent behaviors such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behavior-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviors, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, demonstrating how smart contracts enforce transparent auction rules without trusted intermediaries. Simulations confirm the effectiveness of the proposed model: the dynamic penalty mechanism reduces the profitability of shill bidding while keeping penalties low for honest bidders. Performance evaluation shows that the system introduces only moderate gas and latency overhead, keeping transaction costs and response times within practical bounds for real-world use. The approach provides a practical method for behaviour-based fraud prevention in decentralised systems where trust cannot be assumed.
Weiwei Guo, Hossein Jahanshahloo, Laima Spokeviciute, Qingwei Wang
This paper examines how on-chain factors (number of active wallets, transaction fees, and transaction volume) and off-chain factors (liquidity and investor attention) impact Bitcoin market efficiency from April 2014 to April 2022. We identify three periods in Bitcoinâs market development: development, growth, and additional development stage. We propose three hypotheses: (1) increased investor attention enhances market efficiency, (2) a rise in active users improves efficiency directly and through liquidity and investor attention, and (3) higher transaction fees and on-chain volume positively impact efficiency directly and indirectly. Our findings support these hypotheses during Bitcoinâs development and growth periods. However, in the additional development stage, the total effect of active users, transaction fees, and transaction volume becomes negative when considering mediating effects, and largely insignificant when focusing on direct effects. Additionally, we find increased netflow between whales and exchanges, a proxy for institutional activity, improves efficiency. We conclude that as Bitcoinâs market develops, factors such as changing user composition and increased regulatory scrutiny alter the dynamics of on-chain factors and their influence on market efficiency.
Taylor Lundy, Narun Raman, Scott Duke Kominers, Kevin LeytonâBrown
Conspicuous consumption occurs when a consumer derives value from a good based on its social meaning as a signal of wealth, taste, and/or community affiliation. Common conspicuous goods include designer footwear, country club memberships, and artwork; conspicuous goods also exist in the digital sphere, with non-fungible tokens (NFTs) as a prominent example. The NFT market merits deeper study for two key reasons: first, it is poorly understood relative to its economic scale; and second, it is unusually amenable to analysis because NFT transactions are publicly available on the blockchain, making them useful as a test bed for conspicuous consumption dynamics. This paper introduces a model that incorporates two previously identified elements of conspicuous consumption: the \emph{bandwagon effect} (goods increase in value as they become more popular) and the \emph{snob effect} (goods increase in value as they become rarer). Our model resolves the apparent tension between these two effects, exhibiting net complementarity between others' and one's own conspicuous consumption. We also introduce a novel dataset combining NFT transactions with embeddings of the corresponding NFT images computed using an off-the-shelf vision transformer architecture. We use our dataset to validate the model, showing that the bandwagon effect raises an NFT collection's value as more consumers join, while the snob effect drives consumers to seek rarer NFTs within a given collection.
Open access
2 source records
Consumer Behavior in Brand Consumption and Identification
⢠Matching trading periods and investment horizons between equities and cryptocurrencies are fundamentally challenging. ⢠Monday returns and intermarket connectedness of cryptocurrencies notably differ when alternative benchmark (closing) prices are used. ⢠Using inconsistent return estimation methods from different sources delivers spurious intermarket connectedness results. ⢠THETA, GNO, GLM, ENJ, WAXP, KCS, and WAVES are most vulnerable to the return estimation method. ⢠Seemingly inconsequential choices critically affect the main conclusions drawn by the existing studies on market interconnectedness. Cryptocurrencies trade continuously, unlike traditional assets limited to weekdays, creating challenges in calculating Monday returns. This paper investigates the impact of four benchmark closing pricesâFriday, Saturday, Sunday, and a weekend averageâon intermarket connectedness. Analyzing 72 cryptocurrencies (2018â2024) and their relation to the S&P500 using the TVP-VAR model, we find significant variations in economic and statistical outcomes, influencing both the magnitude and direction of spillovers. Mixed log- and non-log-based return methods yield inconsistent results for specific cryptocurrencies like THETA, GNO, GLM, and WAVES. These findings highlight the critical importance of consistent return methodologies in cryptocurrency market analysis.
Previous research has investigated how certain strategies can influence people's decisions in simple, everyday choices, such as selecting a loaf of bread or purchasing a book online. The objective of this study was to further the understanding on choice architecture elements of default opt-ins and social proof tags, which are interface elements that signal the use of a product by other individuals. We analyzed their effect in an e-commerce context, specifically exploring high-stake economic decision-making that is characterized by high economic cost (financial or opportunity cost) and high decision importance. We achieved this through investigating the effect of default opt-ins on test ride bookings for an automobile, as well as the influence of social proof tags on click-through rates and âbookingsâ, which involve a payment of ~5% of the vehicle price made by customers to reserve a place for them on the purchase waitlist. We hypothesized that a default opt-in in the test ride form would have a significant positive influence on the conversion rate. Our findings supported our hypothesis. We further hypothesized that the addition of social proof tags on choices within a set of alternatives would result in a significant increase in the consideration of those choices for purchase. Though the results were inconclusive, our comparative analysis showed the potential importance of both the default options and social proof tags on consumer decision-making, creating an opportunity for further research into the effective use of the combination of defaults and social proof tags in an e-commerce context.
Abstract Onâchain sealed auctions represent a novel approach to electronic bidding auctions, wherein the introduction of zeroâknowledge proof technology has significantly enhanced the security of auctions. However, most mainstream onâchain sealed auction schemes currently employ Bulletproofs to prove auction correctness, which leaves room for optimization in terms of verification time and inherent security. Addressing these issues, an onâchain sealed auction scheme based on zeroâknowledge succinct nonâinteractive argument of knowledge (zkâSTARK) is proposed. This scheme leverages the decentralization and immutability of blockchain and smart contracts to eliminate thirdâparty involvement while ensuring the security of the auction process. The Inter Planetary File System is utilized to provide a qualification review mechanism for the auctioneer, enabling the screening of unqualified bidders before the auction. Additionally, the scheme employs RSA encryption to conceal bidders' bids, Pedersen commitments to ensure the consistency of bidding information, and zkâSTARKs to verify the correctness of the winning bid. Security analysis and experimental results demonstrate that the proposed scheme meets the required security standards, with time consumption at various stages of the auction being within acceptable limits, and effectively reduces the time required for proof verification.
Cryptocurrencies have found their way into the financial market as a serious alternative in recent years. In particular, Bitcoin is increasingly coming into focus. Currently, however, little is known why people invest in cryptocurrency or not. The present study seeks to shed light on individual difference variables potentially associated with these investment decisions. This includes personality traits, knowledge, and attitudes toward the social and political environment. The effective sample comprised 603 respondents who completed an online survey. Based on the proportion of their financial portfolio invested into Bitcoin, participants were divided into three groups: Non-Bitcoiners, Bitcoin Enthusiasts, and Bitcoin Maximalists. Group comparisons and prediction models indicated that Bitcoiners differed substantially from Non-Bitcoiners in justice-related attitudes as well as in specific knowledge about this cryptocurrency. By contrast, general political attitudes or reinforcement sensitivity did not differ much, and there was hardly a difference in basic dimensions of personality and general knowledge.
Gilbert Fridgen, Roman Kräussl, Orestis Papageorgiou, Alessandro Tugnetti
Abstract This paper analyzes the sales of 875,389 art nonfungible tokens (NFTs) on the Ethereum blockchain to identify the key determinants influencing NFT pricing and market dynamics. We find that market liquidity and trade volume are strong predictors of NFT prices. Contrarily, social media activity negatively correlates with prices. Introducing an artist ranking system, our study reveals a âsuperstar effectâ, with a few artists dominating sales, and herding behaviour within the NFT market.
Marketers across industries appeal to consumersâ need for uniqueness in their marketing and product strategies. While there is an understanding of the many benefits of such a strategy and its underlying mechanisms, the effects are often linked to product scarcity, leaving a productâs distinctiveness compared to similar products unexplored. In this study, we examine the effect of product attribute distinctiveness using transaction data of a large non-fungible token (NFT) collection. Despite identical initial launch prices for all products in the collection, secondary sale prices vary substantially. Using a selection model, our results show that a unique product is less likely to be resold. We also find a positive relationship between attribute distinctiveness and transaction value. This indicates the importance of such product information to consumers. The implications of our empirical study add to the literature on uniqueness, NFTs, and crypto marketing.
Open access
Consumer Market Behavior and Pricing
Digital Marketing and Social Media
Consumer Behavior in Brand Consumption and Identification
Dabao Wang, Bang Ye Wu, Xingliang Yuan, Lei Wu ¡ 6 authors
The prosperity of Decentralized Finance (DeFi) unveils underlying risks, with reported losses surpassing 3.2 billion USD between 2018 and 2022 due to vulnerabilities in Decentralized Applications (DApps). One significant threat is the Price Manipulation Attack (PMA) that alters asset prices during transaction execution. As a result, PMA accounts for over 50 million USD in losses. To address the urgent need for efficient PMA detection, this article introduces a novel detection service,DeFiGuard, using Graph Neural Networks (GNNs). In this article, we propose cash flow graphs with four distinct features, which capture the trading behaviors from transactions. Moreover,DeFiGuardintegrates transaction parsing, graph construction, model training, and PMA detection. Evaluations on the collected transactions demonstrate thatDeFiGuardwith GNN models outperforms the baseline MLP model and classical classification models in Accuracy, TPR, FPR, and AUC-ROC. The results of ablation studies suggest that the combination of the four proposed node features enhancesDeFiGuardâs efficacy. Moreover,DeFiGuardclassifies transactions within 0.892 to 5.317 seconds, which provides sufficient time for the victims (DApps and users) to take action to rescue their vulnerable funds. In conclusion, this research offers a significant step towards safeguarding the DeFi landscape from PMAs using GNNs.