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.
Marcin Wątorek, Paweł Szydło, Jarosław Kwapień, Stanisław Drożdż
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.
Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.
Mathew Fukuzawa, Brandon M. McConnell, Michael G. Kay, Kristin Thoney-Barletta · 5 authors
Purpose Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts. Design/methodology/approach Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results. Findings The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally. Research limitations/implications Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience. Practical implications Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost. Social implications Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams. Originality/value This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.
Ingrid Bauer, Qianyu Liu, Claudio J. Tessone, Gerhard Schwabe
While a wealth of potentially valuable data is generated and stored every year, many businesses suffer from inefficiencies, information asymmetries, and high storage costs, and lack knowledge on how to monetize their data assets. Blockchain is said to offer crucial building blocks to enable a verified, traceable exchange and trading with sensitive data goods and to address current challenges. While the technology's potentials for decentralized data markets have been discussed, the question of how to realize it to optimize trading and welfare remains open. Applying design-science research methods and computational simulation to a real-world business-oriented blockchain project, this study proposes a market model. By adopting the consortium blockchain, we are thinking outside the confines of tokens tied to a blockchain when applying blockchain to the data trading market. Our marketplace is designed outside the speculative tokens space and can focus on the data trading marketplace. We evaluate the effects of different pricing functions on market welfare and trading in on-chain data goods. The results indicate that data trading and welfare can be maximized through a logarithmic pricing function. Further, in a market of heterogeneous agents, unexpectedly, we observe a tipping point in transaction fees above which market operations collapse. Monitoring the market's consumer price elasticity helps us to avoid this collapse node, and we can also impact it by controlling transaction costs. Academics and practitioners can learn about the idiosyncrasies of blockchain in market design and operation.
Dayan A. Perera, Jessica Lim, Shuta Gunraku, Wern Han Lim
This research introduces an innovative approach to forecasting cryptocurrency prices by combining user-generated content (UGC) and sentiment analysis with quantitative data. The primary goal is to overcome limitations in existing methods for market forecasting, where accurate forecasting is crucial for informed decision-making and risk mitigation. The paper suggests a robust prediction methodology by integrating sentiment analysis and quantitative data. The study reviews prior research on sentiment analysis and quantitative analysis of cryptocurrency and stock price prediction. It explores the integration of machine learning and deep learning techniques, an area not extensively explored before. The methodology employs Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Bidirectional LSTM and Gated Recurrent Unit (GRU) models to capture temporal dependencies. Prediction accuracy is assessed using metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and a confusion matrix. Results show that GRU models excel in prediction, while RNN models outperform in predicting price movements; with an emphasis on the significance of a suitable data preprocessing pipeline towards improving model performance. In summary, this study demonstrates the effectiveness of integrating sentiment analysis and quantitative data for cryptocurrency price forecasting using UGC data.
As the global economy continues to grow, more companies are outsourcing their logistics activities to third-party logistics (3PL) providers. This is because consumers have a high demand for various types of goods delivery, \nincluding for small and large packages, light and heavy items, and so on. As a result, package delivery services have become more competitive, offering a range of services to meet these diverse needs. However, this increase in competition has also made it more important for consumers to carefully consider their options and choose a delivery service that is efficient,cost-effective, and reliable. The solution is to use a logistics recommendation system and smart contracts that allow consumers to easily determine and order logistics services according to their needs. Therefore, in this study the author want to state that this paper as proposed paper.
This paper introduces a novel framework for rate discovery in de-centralized finance (DeFi), focusing on the unique challenges andopportunities within decentralized lending platforms. We explorethe mechanisms of interest rate formation in a decentralized en-vironment, free from traditional banking institutions’ control. Byleveraging lending pool dynamics, we propose a method that inte-grates borrowers’ risk profiles with market liquidity conditions todetermine fair borrowing rates without third party involvment. Ourmodel aims to offer a transparent and reliable solution for rate dis-covery in DeFi. Through a series of simulations, we demonstratethe potential of our framework to improve lending practices in theDeFi ecosystem, making it a viable and competitive alternative toconventional financial systems. The findings suggest that our ap-proach not only enhances the transparency and fairness of the lend-ing process but also encourages a more informed participation oflenders and borrowers, ultimately contributing to the stability andgrowth of the DeFi market.
Decentralized exchanges are becoming a competitive necessity for Web3 users. However, they cannot beat centralized exchanges in terms of user experience. Due to expensive gas fees, the blockchain cannot implement the classic order book model which is the pillar for traditional finance exchanges. Instead, most decentralized exchanges operate on the Automated Market Maker (AMM) model using a pre-defined mathematical pricing rule. AMM is more efficient to run on the blockchain but the biggest tradeoff is impermanent loss for liquidity providers and price slippage for traders. This remains the most noticeable drawback of today’s AMM. In this paper, we make the following contributions. First, we observe that if AMM is virtualized as an order book, its “order-book" shape is awkwardly different from that of a real-world order book. We argue that this is conceptually connected to the above weakness. We are thus motivated to design an AMM, the first of its kind, that mimics the price impact behaviors of real-world order books. Second, the proposed AMM, thanks to this property, significantly outperforms the state-of-the-art AMM in impermanent loss. Interestingly, our AMM can even result in impermanent gain. We are also better for large orders where price slippage is a concern. Third, another feature is that, while today’s AMM typically requires a fixed inventory ratio for the liquidity pool, the new AMM allows this ratio to vary, giving liquidity providers flexible options for joining or exiting the pool. All these advantages are offered without losing desirable properties of an AMM regarding split-order exploitation, arbitrage risks, and liquidity continuity. Our findings are validated by theoretical analysis with mathematical proofs and, also, experimental evaluation which was comprehensively conducted using two real-world datasets and a synthetic dataset representing different market scenarios. Our research is the first in the literature on AMM design that factors in statistical properties from the order book model.
Krzysztof Gogol, Robin Fritsch, Malte Schlosser, Johnnatan Messias · 6 authors
This paper studies liquid staking tokens (LSTs) on automated market makers (AMMs), both theoretically and empirically. LSTs are tokenized representations of staked assets on proof-of-stake blockchains. First, we model LST-liquidity on AMMs theoretically, categorizing suitable AMM types for LST liquidity and deriving formulas for the necessary returns from trading fees to adequately compensate liquidity providers under the particular price trajectories of LSTs. For the latter, two relevant metrics are considered: (1) losses compared to holding the liquidity outside the AMM (loss-versus-holding, or "impermanent loss"), and (2) the relative profitability compared to fully staking the capital (loss-versus-staking) which is specifically tailored to the case of LST-liquidity. Next, we empirically measure these metrics for Ethereum LSTs across the most relevant AMM pools. We find that, while trading fees often compensate for impermanent loss, fully staking is more profitable for many pools, raising questions about the sustainability of the current LST liquidity allocation to AMMs.
Blockchain technology has been adopted to improve traceability and authenticity in wine supply chains (WSCs). However, whether through outsourcing or self-implementation of a blockchain-based wine traceability system (BTS), there are significant costs involved, as well as concerns regarding consumer privacy. Motivated by observations of real-world practice, we explore the value of blockchain in enhancing traceability and authenticity in WSCs through a Stackelberg game-theoretical analysis. By comparing the equilibrium solutions of the scenarios with and without blockchain, we uncover the value of blockchain in tracing wine products. Our findings show that blockchain adoption can increase WSC prices under certain conditions. We derive the threshold for a third-party BTS service fee that determines blockchain adoption for tracing wine products and reveal the moderating effect of consumer traceability preferences and privacy concerns. Furthermore, the investigation of who should lead the implementation of BTS finds that the equal cost sharing between the manufacturer and the retailer results in no difference in BTS implementation leadership. Otherwise, the manufacturer always benefits from taking the lead in the implementation of BTS, and the retailer should undertake a leadership role in BTS implementation if they need to bear higher costs.
Currently, over 90% of Ethereum blocks are built using MEV-Boost, an auction that allows validators to sell their block-building power to builders who compete in an open English auction in each slot. Shortly after the merge, when MEV-Boost was in its infancy, most block builders were neutral, meaning they did not trade themselves but rather aggregated transactions from other traders. Over time, integrated builders, operated by trading firms, began to overtake many of the neutral builders. Outside of the integrated builder teams, little is known about which advantages integration confers beyond latency and how latency advantages distort on-chain trading. This paper explores these poorly understood advantages. We make two contributions. First, we point out that integrated builders are able to bid truthfully in their own bundle merge and then decide how much profit to take later in the final stages of the PBS auction when more information is available, making the auction for them look closer to a second-price auction while independent searchers are stuck in a first-price auction. Second, we find that latency disadvantages convey a winner's curse on slow bidders when underlying values depend on a stochastic price process that change as bids are submitted.
Many e-commerce platforms, such as Amazon and JD.com, consider product information disclosure as a crucial retail strategy. However, due to potential consumer distrust in the disclosed information, these platforms may employ blockchain technology to validate the information and alleviate consumers’ doubts. This study presents a game-theoretical model to investigate the interaction among the manufacturer’s choice of selling mode, the platform’s decision regarding blockchain adoption, and information disclosure strategies in the presence of information asymmetry. Optimal pricing and information disclosure strategies are derived, and the impact of various parameters on the equilibrium results is analyzed. We find that the consumers’ privacy concerns and blockchain implementation costs both have negative effects on the equilibrium retail price, disclosed information amount and profits of the manufacturer and platform. Moreover, we also find that the introduction of blockchain has different effects on a platform’s pricing and information disclosure strategies depending on the market size. In addition, conditions are identified under which the manufacturer selects the optimal selling mode and the platform makes an optimal decision on blockchain adoption. The findings demonstrate that the cost of consumers’ privacy concerns and the commission rate play significant roles in determining the equilibrium selling mode selection and blockchain adoption strategies.
Purpose Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research. Design/methodology/approach A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria. Findings The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors. Originality/value This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.
Purpose This review aims to provide an overview of research from different academic disciplines to chart some of the key developments in retail cryptocurrency trading against the backdrop of the wider trading landscape, and how it has evolved in recent years. The purpose of this review is to provide researchers with a broad perspective to highlight the complex range of factors that drive cryptocurrency trading among retail investors. Design/methodology/approach Peer-reviewed literature from the social sciences, economics, marketing and branding disciplines is synthesised to explicate influential factors among retail cryptocurrency investors. Findings Online retail trading communities can create narratives that ascribe value to cryptocurrencies leading to consumer herding behaviours. The principles that underpin emotional branding and Fear of Missing Out can promote trading behaviour driven by heuristic processing and cognitive biases. Concurrently, the tenets of controversial marketing and the anti-establishment nature of Bitcoin and other cryptocurrencies serve to bolster in-group out-group categorisations fostering continued investment and market volatility. Consequently, Bitcoin and cryptocurrency trading more broadly offer a powerful combination of excitement from risk-taking akin to gambling buffered by the sanctity of social inclusion. Originality/value A broader, unique perspective on retail cryptocurrency trading which assists in better understanding the complexities that underpin its appeal to retail investors.
Kripto para ve spor arasında çeşitli ilişkiler bulunmaktadır. Kripto para birimleri, spor dünyasında sponsorluk ve reklam fırsatları sunarak önemli bir rol oynamaktadır. Spor takımları, kripto para şirketleriyle anlaşmalar yaparak forma reklamlarında veya stadyum isim haklarında kripto para şirketlerinin markalarını sergileyebilirler. Ayrıca, kripto para birimleri spor etkinliklerinin bilet satışlarında da kullanılabilmektedir. Bazı spor organizasyonları, kripto para birimleriyle bilet satın alma ve ödeme imkânı sunarak, kullanıcıların daha hızlı ve kolay bir şekilde biletlerini temin etmelerini sağlayabilirler. NFT'ler (Non-Fungible Token), kripto para birimleriyle popülerlik kazanan bir konudur ve spor dünyasında da büyük ilgi görmektedir. Bazı çevrimiçi bahis platformları, kripto para birimlerini kabul ederek, kullanıcıların anonimliklerini korumasını ve hızlı işlem yapmalarını sağlayabilirler. Ünlü sporcuların kripto para yatırımlarıyla ilgili açıklamaları veya sosyal medya üzerinden kripto para birimlerini desteklemeleri, kripto para sektöründe ilgi ve bilinirlik oluşturabilir. Kripto para ve spor arasındaki ilişki, spor dünyasına yeni fırsatlar ve finansal yenilikler getirebilirken, aynı zamanda riskleri ve belirsizlikleri de beraberinde getirebilir. Ancak, bu ilişkinin gelecekte daha da gelişmesi ve spor endüstrisinde daha fazla etkileşim sağlaması beklenmektedir.
Thomas Brzustowski, Alkis Georgiadis-Harris, Balázs Szentes
This paper reconsiders the problem of a durable-good monopolist who cannot make intertemporal commitments. The buyer’s valuation is binary and his private information. The seller has access to dynamic contracts and, in each period, decides whether to deploy the previous period’s contract or to replace it with a new one. The main result of the paper is that the Coase conjecture fails: the monopo-list’s payoff is bounded away from the low valuation irrespective of the discount factor. (JEL D42, D82, D86, L12)
The main aim of this article is to present and analyse changes in the global luxury goods market in the 21st century, with particular emphasis on the effects of the supply shock caused by the COVID-19 pandemic. In the part devoted to the period before the pandemic, the following market trends will be described: the democratisation of luxury, brand consolidation process, the emergence of Generation Alpha, the evolution of masstige goods relied on fast-fashion processes, "Chinese bulimia", market ”retailization” processes, the growing importance of mono-brand stores, rejection of online sales by European brands, e-commerce development, and growing environmental awareness. The article’s part, dedicated to the period of the pandemic and the changes immediately after it, presents trends such as the emergence of non-fungible tokens (NFTs) and gaming goods (metaverse gaming), the rebirth of the vintage market and secondhand stores, an increase in sales of casual goods, new multi-brand sales platforms, and the growing importance of local markets. The article is based on literature query and comparative analysis of industry reports prepared by Deloitte, Bain Company, and Luxe Digital.
Open access
Consumer Behavior in Brand Consumption and Identification
Purpose The purpose of this research is to investigate how to introduce a financing scheme to tackle the manufacturer's capital constraint problem, discuss the effects of data-driven marketing (DDM) quality, cross-channel-return (CCR) rate and financing interest rate on the members' pricing and delivery-lead-time decisions and optimal performances, and analyzes `how to achieve the coordination within a dual-channel supply chain (DSC) by contract coordination. Design/methodology/approach This work establishes a DSC model with DDM, and the offline retailer can provide internal financing to the capital-constrained online manufacturer. The demand under the price is determined based on DDM quality, customer channel preference and delivery lead time. Then, combined with the Stackelberg game, the optimal pricing and delivery-lead-time decisions are discussed under the inconsistent and consistent pricing strategies with decentralized and centralized systems. Furthermore, it designs a manufacturer-revenue sharing contract to coordinate the members under the two pricing strategies. Findings (1) The increase of DDM quality will reduce the delivery-lead-time under the inconsistent or consistent pricing strategy and will push the selling prices; (2) The growth of the CCR rate will raise selling prices and extend the delivery-lead-time under the decentralized decision; (3) Under price competition, the offline selling price is higher than the online selling price when customers prefer the offline channel and vice versa; (4) The retailer and the manufacturer can achieve a win-win situation through a manufacturer-revenue sharing contract. Originality/value This paper contributes to the studies related to DSC by investigating pricing and delivery-lead-time decisions based on DDM, CCR, internal financing and supply chain contract and proposes some managerial implications.
Blockchain tokens have accumulated tremendous market value but remain highly controversial, given their price volatility and seemingly speculative nature. Ironically, this very characteristic can foster token retention as users wait for occasions of appreciation. In this paper, we conduct an empirical analysis with 58 tokens in two steps: first, an investigation of the drivers of user activity and token price volatility using a new blockchain token classification framework, searching for possible tokenomics links. Our findings suggest that there is an intrinsic relationship between the way tokens are used as a means of exchange and how token usage dynamics influence user engagement oppositely to market stability. Only some features, such as earning potential and voting rights, foster token-holding strategies, while only Ethereum ecosystem membership has positive effects on price volatility. Second, we analyze the direct relationship between price volatility and active users. Results show that, on average, a 10% increase in volatility is related to a decrease in active addresses ranging between 3.96% and 5.88%. The finding is supportive of the hypothesis that token price volatility may be treated as an opportunity to increase token retention.