Purpose This paper aims to investigate how brands emerge and acquire value in decentralized digital ecosystems and examines the role of electronic word-of-mouth (eWOM) on social media in shaping their market interest and valuation. Design/methodology/approach The research design considers two studies: one exploratory, based on interviews with Web3 experts and the other on econometric analysis of data from X (13 million tweets relating to 7 Web3-native brands) and OpenSea (315 million nonfungible token [NFT] transactions). Findings The findings show that brand value formation in decentralized environments rests on three layers: an asset-based value layer, a network-based value layer and a marketplace-based value layer. The research demonstrates that both the volume and valence of eWOM independently influence consumer awareness, desirability and the value or price of NFT-based brands in the marketplace. However, their interaction, or an excessively positive eWOM, can have a counterproductive effect, signaling hype and reducing perceived credibility in speculative digital markets. Research limitations/implications The research contributes to the emerging Web3 literature by proposing an empirical framework for decentralized brand value formation in digital ecosystems. The framework captures how brand value is generated and how eWOM and marketplace trading and activities influence this value in decentralized environments. However, the analysis is based on seven Web3 native brands and on data gathered in the context of a bull market. Practical implications The study offers brand managers empirical insights into branding in decentralized ecosystems. It provides operational strategies for decision-makers who are seeking to develop effective branding strategies in emerging decentralized marketplaces. Social implications The study shows that decentralized branding empowers users and online communities to shape Web3 brand value. Originality/value The study contributes to the growing understanding of decentralized branding by showing how brand value and eWOM dynamics shape brand creation in decentralized and speculative markets. It shows that digital discourse can simultaneously function as a mechanism of brand amplification and as a potential signal of speculative overvaluation. To the best of the authors’ knowledge, it is the first study that proposes an empirical investigation of the value of decentralized branding and its relation to eWOM.
V Vishnu Prasad, Meta Dev Prasad Murthy, Rishika Jain
Premium non-fungible token (NFT) collections often fail to attract liquidity, while modest but coherent ones thrive, presenting an anomaly that classical signaling cannot explain. We reframe market-making as a coordination problem and introduce a Brand × Topology × Dispersion (BTD) framework, arguing that participation follows weakest-link clarity: the least clear signal dimension, not the average, governs action. A high-realism 2 × 2 × 2 experiment (N = 336) shows that brand capital, ownership topology, and value dispersion each raise willingness to trade, yet the minimum across them dominates conversion; discordant signals depress engagement more than concordant signals lift it; and signals act as complements in thin markets but substitutes in mature ones. A 6-month Ethereum panel, analyzed with fractional logit and Cox hazard models, replicates these patterns in the field. The studies extend signaling theory from dyadic quality revelation to multilateral coordination and yield a bottleneck-governance principle for marketers and platforms, suggesting that the weakest clarity dimension be repaired first.
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-Sánchez · 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.
Το αυξανόμενο ενδιαφέρον για εναλλακτικές επενδύσεις που προσφέρουν διαφοροποίηση και υψηλότερες αποδόσεις, καθοδηγείται από τους περιορισμούς των παραδοσιακών χρηματοπιστωτικών αγορών και τις εξελισσόμενες ανάγκες των σύγχρονων επενδυτών. Στην παρούσα διδακτορική διατριβή διερευνώνται οι μηχανισμοί μεταβολής των τιμών δύο τομέων εναλλακτικών επενδύσεων: των επενδύσεων με κριτήρια Περιβάλλοντος, Κοινωνίας και Διακυβέρνησης (Environmental, Social, and Governance – ESG), με έμφαση στις εισηγμένες εταιρείες που επιδεικνύουν περιβαλλοντική υπευθυνότητα, και των ψηφιακών περιουσιακών στοιχείων που βασίζονται στην τεχνολογία Blockchain, μέσα από ένα ενοποιημένο μεθοδολογικό πλαίσιο. Το πρώτο μέρος της διατριβής εξετάζει τις χρηματοοικονομικές επιπτώσεις της Εταιρικής Περιβαλλοντικής Υπευθυνότητας (Corporate Environmental Responsibility – CER) στις επιχειρήσεις του δείκτη S&P 500 κατά τη διάρκεια δεκαπέντε ετών. Αξιολογείται η επίδραση της περιβαλλοντικής επίδοσης στην αποτίμηση της αγοράς μέσω δεικτών προσαρμοσμένων στον κίνδυνο, οι οποίοι βασίζονται στο υπόδειγμα CAPM και στην υπόθεση της αποτελεσματικής αγοράς. Η ανάλυση, η οποία στηρίζεται σε τεχνικές παλινδρόμησης δεδομένων πάνελ, όπως οι εκτιμήσεις OLS και 3SLS, εισάγει την έννοια του «Πράσινου Premium» — ενός μετρήσιμου αντισταθμίσματος μεταξύ περιβαλλοντικής υπευθυνότητας και αποδόσεων των επενδυτών. Τα αποτελέσματα δείχνουν ότι, ενώ η CER συσχετίζεται θετικά με λειτουργικούς δείκτες, όπως οι πωλήσεις και τα κέρδη, η χρηματιστηριακή απόδοση παραμένει κατώτερη, γεγονός που υποδηλώνει ένα διαρκές μειονέκτημα για τις επιχειρήσεις με περιβαλλοντικό προσανατολισμό στα μάτια των επενδυτών. Το δεύτερο μέρος της διατριβής επικεντρώνεται στη χρηματοοικονομική διάσταση του Blockchain, παρέχοντας μια ολοκληρωμένη ανάλυση της συμπεριφοράς τιμών των Μη Εναλλάξιμων Διακριτικών (Non-Fungible Tokens – NFTs) μέσω σύγχρονων μεθόδων μηχανικής μάθησης. Δεδομένα που αντλήθηκαν απευθείας από πλατφόρμες Blockchain και μετασχηματίστηκαν μέσω προηγμένων τεχνικών μηχανικής χαρακτηριστικών, χρησιμοποιούνται για την αξιολόγηση της προβλεπτικής ικανότητας των μοντέλων Random Forest, XGBoost και Πολυεπίπεδου Αντιληπτή (Multilayer Perceptron). Το τελικό σύνολο δεδομένων προέκυψε από μια προηγμένη διαδικασία μετασχηματισμού χαρακτηριστικών, εμπλουτισμένη με σύνθεση χαρακτηριστικών βάσει εξειδικευμένης γνώσης και Deep Feature Synthesis, αναδιαμορφωμένο μέσω Ανάλυσης Κύριων Συνιστωσών (Principal Component Analysis – PCA) και βελτιστοποιημένο μέσω τεχνικών επιλογής χαρακτηριστικών για βέλτιστη ερμηνευσιμότητα και μείωση διαστασιμότητας. Από τα μοντέλα που εξετάστηκαν, το XGBoost παρουσίασε τη μεγαλύτερη ακρίβεια πρόβλεψης, με τα ιστορικά δεδομένα τιμών να αποτελούν τον πιο καθοριστικό παράγοντα πρόβλεψης. Συνολικά, τα δύο μέρη της διατριβής συμβάλλουν στη διεύρυνση της βιβλιογραφίας σχετικά με τις μη παραδοσιακές κατηγορίες επενδυτικών στοιχείων, μέσω της εφαρμογής ισχυρών αναλυτικών μεθόδων σε διαφορετικά επενδυτικά πεδία. Τα αποτελέσματα παρέχουν εξειδικευμένες γνώσεις σχετικά με τους παράγοντες που διαμορφώνουν την αξία των περιουσιακών στοιχείων και θεμελιώνουν ένα μεταβιβάσιμο μεθοδολογικό πλαίσιο για την πρόβλεψη τιμών περιουσιακών στοιχείων σε αναδυόμενες και ετερογενείς αγορές.
Non-fungible token (NFT) marketplaces are the main venues for NFT transactions. In recent years, these platforms have introduced ”Sweeping,” 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.
Emmanuel L. C. VI M. Plan, Doan Binh Minh Do, Xuan Trung Pham, Lê Khánh Linh Vũ · 5 authors
Wash trading is a major issue in non-fungible token (NFT) markets that distorts transaction volumes and returns. In this work, we examined the effect of wash trading by focusing on two specific NFT collections. First, we implemented a multi-layered wash trading detection algorithm to identify wash trades. Using regression analysis, we then showed that weekly transaction volumes of a heavily wash-traded collection can be magnified by two orders of magnitude compared to a cleaner collection. Moreover, wash trading resulted in positive returns in a collection that has extensive wash trading; in contrast, wash trading in the cleaner collection was penalized with negative returns, suggesting heterogeneity in both incidence and profitability of wash trading. Our findings provide a better understanding on the impact of wash trading on NFT markets and highlight the need to improve security in NFT markets and other decentralized financial technology systems. In particular, by extending transaction-level detection to collection characteristics, we could assess the impact of wash trading. This approach is easily replicable and enables market stakeholder to identify inauthentic activity and policy makers to provide adequate safeguards for these products.
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.
The extreme and often seemingly irrational pricing of non-fungible tokens (NFTs) has inspired interest in identifying their valuation determinants. While visual features have been proposed as partial determinants for NFT price variation, previous studies often rely on non-interpretable models and fail to control for a critical confounder: NFT collection identity. In this paper, we analyze over 160,000 NFTs across 32 major collections to evaluate the explanatory power of visual features for NFT pricing. We show that visual models achieve high predictive performance only when collection label leakage is present, falsely inflating results by learning collection-specific price tiers. Once collection identity is controlled, we find that visual featuresinterpretable or deep-learning based-offer limited generalizability and weak predictive power across collections. However, for a subset of collections, visual features do exhibit modest withincollection predictability, a phenomenon we term photorelevance. We introduce a framework to quantify photorelevance and identify two collection-level characteristics-image contrast variance and price variance-as statistically significant predictors of it. Our findings highlight the methodological importance of addressing collection label leakage and suggest that aesthetic attributes play a limited and only localized role in NFT valuation. Code and data are publicly released to enable reproducibility and further research.
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 Ellis‐Chadwick, Jοãο S. Oliveira
ABSTRACT This paper explores consumers' drivers and motivations behind luxury‐fashion non‐fungible 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 two‐step 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 semi‐structured 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 text‐based measure of peer opinions constructed from cryptocurrency‐related 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 risk‐neutral 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 Pokémon 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 – PSA (United States), CCIC (China), and SQC (Thailand) – 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.
With the rise of decentralized finance (DeFi), liquidity pools have become essential components in token exchange processes on platforms like Uniswap and Sushiswap. These pools enable users to earn returns through fees and encourage active participation in decentralized markets. This study collects historical data from various liquidity pools. These data are examined to derive insights into investor behavior within these pools. Additionally, the analysis focuses on assessing the returns and risks associated with such investments. By providing a comprehensive perspective on these critical aspects, this research aims to guide liquidity providers in making informed decisions while promoting the growth of sustainable and user-centric liquidity pool ecosystems in the DeFi space.
ABSTRACT In the burgeoning marketplaces of digital assets, non‐fungible tokens (NFTs) revolutionize digital asset ownership and intellectual property (IP) protection, but high minting costs create barriers to marketplace entry and growth. This study examines the impact of “lazy minting,” a new NFT production method introduced by major NFT marketplaces to lower minting costs by deferring blockchain certification until the first sale. In response to the call for further research on emerging technologies in operations management, we explore how this policy affects the net sales performance of existing sellers in the NFT marketplaces. Based on transaction cost economics (TCE) and the literature about different IP protection methods, we distinguish between lazy‐ and regular‐minted NFTs by their differential transaction costs and utilize the staggered difference‐in‐differences (DID) method to conduct our analysis. We find that lazy minting adoption significantly boosts the net sales performance of existing sellers. This is attributed to their cost‐adaptive IP protection behavior. Specifically, they achieve this by minting more NFTs with a larger proportion of style‐consistent NFTs through lazy minting, while strategically employing regular minting for style‐breaking NFTs, which is contingent upon their reputation. Our study has important theoretical and practical implications for operations management under the emerging technological revolution.