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.
The evolution of decentralized platforms has introduced significant advancements in auction systems; however, these advancements bring forth complex challenges in collateral management within sealed-bid auctions. Traditional approaches, reliant on static collateral, often fail to account for dynamic market conditions and participant behavior, thereby limiting participation from individuals with restricted capital and reducing overall auction efficiency. This research identifies a critical gap in dynamic collateral management for decentralized sealed bid auctions and proposes a novel framework to address these challenges. The proposed method integrates dynamic collateral management within the Riggs-TC (Timed Commitment) protocol, enhancing its existing capabilities to handle collateral more flexibly. By leveraging cryptographic advancements, specifically Pedersen Commitments and Zero-Knowledge Proofs, the framework ensures that collateral adjustments are made in real-time, reflecting each auction phase and participant actions effectively. This method not only secures bid confidentiality but also upholds the integrity and fairness of the auction process. Empirical results from deploying this framework demonstrate a significant reduction in entry barriers for participants, an increase in capital efficiency, and heightened security and fairness across the auction lifecycle. Additionally, the framework's adaptability to various decentralized environments suggests its broad applicative potential beyond the initial case studies.
As the demand for cryptographic assets increases, so does the number of fraudulent transactions, necessitating efficient detection methods. In this paper, we propose a method to determine whether a set of transaction data contains fraudulent transactions. We apply topological data analysis, which characterizes the geometric structure of the data, to Ethereum, one of the crypto assets. Our aim is to solve the imbalance in the transaction data used in machine learning models for fraudulent transaction detection. Our method achieved an F1 score of 0.9891 on a set of transaction data containing 10 fraudulent transactions out of 10000 transactions.
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.
With the rapid development of e-commerce, a large number of online platforms specialising in fresh product have entered the market. Previous studies have rarely considered how to efficiently deliver the productâs freshness status to customers. In this paper, we establish a game model that consists of two competitive online platforms, which operate vertically differentiated fresh product on the same market. The product freshness is uncertain due to the opaque delivery process and consumers may have the risk-averse attitude, while platforms could opt to deploy blockchain to trace the productâs logistic information in real time and eliminate the consumerâs risk-averse attitude. Our finding shows that when the platform competition and the consumerâs risk-averse attitude are high, both platforms would like to adopt blockchain technology; With the moderate platform competition and the consumerâs risk-averse attitude, neither platform wants to adopt blockchain technology. Otherwise, only one platform would like to adopt blockchain technology. Moreover, when one platform deploys blockchain, the other one can free ride and become better off. Finally, we analyse consumer surplus and social welfare under different scenarios. A win-win-win situation can be achieved among platforms, consumer surplus, and social welfare when both platforms deploy blockchain technology.
Purpose The Internet of Things (IoT) platform empowers the digital transformation of the manufacturing industry by providing information technology services. Simultaneously, it enters the market by offering smart products to consumers. In light of different service fee scenarios, this article explores the optimal decision-making for the platform. It investigates the pricing models and entry decisions of IoT platforms. Design/methodology/approach In this study, we have formulated a game-theoretic model to scrutinize the influence of the IoT platform ventured into the smart device market on the pre-existing suppliers operating under subscription-based and usage-based pricing agreements. Findings Our outcome shows that introducing an IoT platformâs smart device has a differential effect on manufacturers depending on their contract type. Notably, our research indicates that introducing the platformâs own smart device within the subscription-based model does not negatively impact the profitability of incumbent manufacturers, so long as there is a noticeable discrepancy in the quality of the smart devices. However, our findings within the usage-based model demonstrate that despite the variance in smart device quality differentiation, the platformâs resolution to launch their device and impose their pricing agreements adversely affects established manufacturers. Additionally, we obtain valuable Intel regarding the platformâs entry strategies and contractual inclinations. We demonstrate that the platform is incentivized to present its smart device when reasonable entry costs remain. Furthermore, the platform prefers subscription-based contracts when the subscription fee is relatively high in non-platform entry and entry cases. Originality/value These findings hold significant practical implications for firms operating in an IoT-based supply chain.
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.
Mohd Amirul Helmi Ismail, Syamsul Bahrin Zaibon, Mohd Noor Abdul Hamid, Siti Irna Mustajap ¡ 5 authors
This study investigates how consumer perceptions affect decision-making in the purchase of Non-Fungible Tokens (NFTs), providing a detailed analysis of the factors driving consumer behavior in this emerging market. Using a mixed-methods approach, this study conducted surveys and interviews with NFT purchasers to capture a comprehensive view of their decision-making processes. Our findings reveal that factors such as perceived value, trust in blockchain technology, and the influence of community engagement significantly impact purchasing intentions. These insights contribute to the existing literature by delineating specific consumer behaviors and motivations in the NFT space, highlighting the importance of community trust and perceived technological robustness. Additionally, the study offers practical implications for businesses in the NFT sector, suggesting that establishing strong, credible relationships within the NFT community, and staying abreast of technological advancements are pivotal strategies for maintaining a competitive advantage. By integrating with NFT communities and leaders, businesses can glean trends and collaborative opportunities, fostering innovation, and market leadership in the dynamic NFT landscape.
Abstract Research on cryptocurrency exchanges, consisting of both centralized exchanges (CEXs) and decentralized exchanges (DEXs), has seen a significant increase in contributions in recent years, driven by growing interest in the conceptual design of cryptocurrency markets. Through a comprehensive review of literature published between January 2019 and September 2023, I identify and analyze different dimensions of the ongoing CEX vs. DEX debate. While DEXs emphasize decentralization, user control, and resistance to censorship, CEXs offer higher liquidity, advanced trading features, and a more established track record. Regulatory challenges, such as Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance, also feature prominently in the literature and influence the choice of exchange for both traders and policymakers. In addition, I observe a growing interest in the design of pricing functions for CEXs and DEXs, particularly in the area of automated market makers (AMMs). Finally, based on my findings, I outline future research opportunities in this context and derive research gaps as well as recommended actions for practitioners.
Abstract In this study, we analyzed social media conversations during the unfolding of the FTX crisis, the biggest cryptocurrency scandal in United States history. Drawing on the accessibilityâdiagnosticity framework, we examined the negative spillover effect of the crisis using a natural language processing approach. We specifically assessed whether there was a negative spillover from FTX to other crypto entities with different levels of diagnostic attribute similarity. We collected a large corpus of Twitter conversations related to the FTX collapse in 2022 and used the association rule analysis to determine the association between FTX and other crypto entities. Our analysis revealed that the number of tweets mentioning FTX and other crypto entities changed in line with a series of realâworld events during the FTX crisis. The negative spillover of the FTX crisis occurred primarily during the first 10 days as the FTX scandal unfolded. The results indicated that the FTX crisis spilled over to highly accessible and diagnostic crypto entities, such as Binance, Bitcoin, and the cryptocurrency industry in general. On the other hand, less accessible and less diagnostic crypto entities/currencies like Ethereum and Coinbase did not experience negative spillover from the scandal.
Marcin WÄ torek, PaweĹ SzydĹo, JarosĹaw KwapieĹ, StanisĹaw DroĹźdĹź
The non-fungible token (NFT) market emerges as a recent trading innovation leveraging blockchain technology, mirroring the dynamics of the cryptocurrency market. The current study is based on the capitalization changes and transaction volumes across a large number of token collections on the Ethereum platform. In order to deepen the understanding of the market dynamics, the collection-collection dependencies are examined by using the multivariate formalism of detrended correlation coefficient and correlation matrix. It appears that correlation strength is lower here than that observed in previously studied markets. Consequently, the eigenvalue spectra of the correlation matrix more closely follow the Marchenko-Pastur distribution, still, some departures indicating the existence of correlations remain. The comparison of results obtained from the correlation matrix built from the Pearson coefficients and, independently, from the detrended cross-correlation coefficients suggests that the global correlations in the NFT market arise from higher frequency fluctuations. Corresponding minimal spanning trees (MSTs) for capitalization variability exhibit a scale-free character while, for the number of transactions, they are somewhat more decentralized.
Everyday life depends heavily on the supply chain, and its traceability guarantees the quality and safety of the products. Thus, there is a pressing need for an effective and trustworthy solution to enhance logistic traceability. Traditional traceability systems suffer from low tracking efficiency and inconsistent data. However, the developing blockchain technology promises to improve these issues by being transparent, tamper-proof, and decentralised. This article analyses previous research, highlights problems, and investigates logistic traceability options based on blockchain. First, the conventional traceability approach and stakeholder demands are explained, along with the fundamentals of blockchain technology. Next, a thorough evaluation and analysis of the current publications and enterprise applications is conducted. Lastly, difficulties and potential lines of inquiry are explored. Subsequent studies may concentrate on developing focused consensus processes, creating suitable access controls, examining the function of regulators in the supply chain, etc. This analysis demonstrates that although there are still many obstacles to overcome, blockchain offers a lot of promise to solve traceability problems.