The adoption of cryptocurrency as a payment instrument by firms has sparked ongoing debates about how such strategic moves are perceived by key stakeholders. This study investigates how investors react when an e-commerce firm adds or withdraws from providing cryptocurrency as a payment option. To explore these aspects, we examine two cases: MercadoLibreâs decision to introduce Meli DĂłlar as a payment option, representing the inclusion of cryptocurrency, and eBayâs withdrawal from the Libra project, representing strategic exclusion. We assess the causal impact of these strategies by employing a Regression Discontinuity Design (RDD) and deriving the observation period by using an optimal bandwidth method. The results indicate that there was an immediate decline in share prices following the adoption of the Meli DĂłlar as a payment instrument and an immediate increase following the decision to withdraw from using Libra as a payment instrument. The findings suggest that including cryptocurrency as a payment method may run counter to investor expectations. This study contributes to the discourse on the viability of cryptocurrency adoption by e-commerce firms and emphasizes the importance of understanding how decisions around cryptocurrency convey market signals, which may have strategic implications for a firmâs overall strategy.
With the advancement of blockchain technology, smart contracts are increasingly applied in finance, supply chain, healthcare, and other domains. However, the demand for multi-party data joint analysis within these contracts faces challenges of privacy leakage and malicious deception. This paper applies secure multi-party computation (MPC) to smart contracts, based on the secure computation of intersection, union, and difference of multi-party multisets (IUDMM), to protect data privacy while supporting joint analysis. Existing multiset computation protocols are primarily designed for two-party scenarios under the semi-honest model, which are unsuitable for applications involving multiple participants in smart contracts. This paper introduces a protocol for IUDMM under the semi-honest model, utilizing the multi-key NTRU encryption algorithm and a novel vector encoding method. Furthermore, to address potential malicious behaviors, an IUDMM protocol under the malicious model is designed by incorporating digital commitment method. The protocolâs correctness is analyzed, and its security is proven using the real/ideal model paradigm. Additionally, it also resists collusion attacks by any party. Finally, efficiency analysis and experimental simulations demonstrate that the proposed protocols are efficient, reliable, and fair, providing a secure and practical solution for multi-party data joint analysis and privacy protection in smart contracts.
The evolution of the metaversea collective virtual environment with shared immersion that includes virtual reality (VR), augmented reality (AR), blockchain, and internet technologieshas created new entrepreneurial opportunities, particularly in virtual real estate. Metaverse real estate is blockchain-backed virtual land parcels that can be bought, sold, developed, and rented out in virtual worlds such as Decentraland, The Sandbox, and others. Unlike traditional physical property, ownership in the metaverse is guaranteed through non-fungible tokens (NFTs) to enable transparent and irrevocable proof of ownership. The new digital asset class has created novel entrepreneurial opportunities, including property development, virtual renting, event planning, advertising, and real estate services for virtual properties.Immersive technologies are utilized by entrepreneurs here to develop interactive 3D environments, so it is now possible to provide customers with experiences that are not limited by geography and physics. Virtual real estate development involves building interactive digital properties such as virtual offices, malls, galleries, and entertainment hubs, which can be commercialized via rentals, ticketing, sponsorships, and advertising. Early adopters have realized significant returns on investments, with some virtual plots appreciating by over 500% within months, underscoring the lucrative potential of this emerging market. The metaverse also fosters a democratized and inclusive entrepreneurial ecosystem by lowering classical entry barriers. Virtual businesses require less physical infrastructure and less up-front investment, and entrepreneurs can experiment, prototype, and test business models with less capital exposure. Moreover, the global connectedness of the metaverse enables collaboration across heterogeneous expertise and markets, accelerating innovation and business scaling. This review paper integrates available scientific literature and scrutinizes entrepreneurship in metaverse real estate in terms of market dynamics, technological underpinnings, entrepreneurial strategies, challenges, and opportunities
Ensuring secure and efficient authentication in Vehicular Ad Hoc Networks (VANETs) is vital for real-time communication and network resilience. However, traditional authentication mechanisms, such as Elliptic Curve Cryptography (ECC) and Public Key Infrastructure (PKI), face significant challenges, including high computational overhead, complex certificate revocation, and vulnerability to quantum attacks. To overcome these limitations, we propose a lattice-based authentication protocol that integrates post-quantum cryptography (PQC), zero-knowledge proofs (ZKPs), and fog computing for secure Vehicle-to-Roadside (V2R) communication. Our protocol offers quantum resistance, decentralized authentication, and dynamic pseudonym updates, enhancing both security and privacy in VANETs. Performance evaluations demonstrate that our approach achieves lower message delay (0.8), reduced packet loss ratio (0.6), minimal communication overhead (0.7), and the fastest authentication delay (0.5) compared to ECC and Physically Unclonable Function (PUF)-based methods. Additionally, formal security analysis confirms that our scheme effectively mitigates impersonation, replay, tracking, and quantum attacks, ensuring a scalable and future-proof authentication mechanism for next-generation VANETs.
This study examines the evolving landscape of art patronage in India amidst the digital transformation, with a focus on the traditional and Non-Fungible Token (NFT) art markets. Drawing on data from the Artnet Art Market Report spanning from 2019 to 2023, the research employs quantitative analysis to compare sales volumes, average prices, regional distribution of buyers, gender representation, artist mediums, market sentiment, and platform dominance. Key findings include the exponential growth of NFT art sales volumes, the premium associated with digital artworks, and the urban-centric nature of NFT art patronage. Gender disparities in art patronage and the dominance of digital artists in the NFT market also emerge as significant trends. The implications of these findings underscore the importance of adapting to digital transformation trends, promoting inclusivity and accessibility within the art community, and leveraging digital platforms for growth and innovation. By bridging the gap between traditional and digital art markets, this research contributes to a deeper understanding of the cultural, social, and economic implications of digital technologies in the arts.
The popularity of cryptocurrencies as alternative investments has grown in recent years. However, it remains unclear whether cryptocurrency investors behave irrationally in a similar way to emerging market investors. Using a systematic literature review, this study aims to compare the factors related to the presence of behavioural biases in the cryptocurrency and emerging stock markets. This study highlights similarities and differences between cryptocurrency and emerging stock market investor behaviour. Thus, the study's novelty arises from comparing the role of behavioural inclinations in cryptocurrency and emerging stock markets. The findings indicate that the small amount or lack of available information about small-cap emerging stocks or cryptocurrencies may reinforce investor sentiment and herding behaviour. The herding behaviour among investors in both markets may stem from following the most popular investment trends. Investors in cryptocurrency and emerging stock markets also tend to overreact to market sentiment and changes in market conditions. Extreme market conditions may affect the strength of herding behaviour, disposition effect, price clustering, anomalous behaviour, investor sentiment and uncertainty. Thus, cryptocurrency and emerging stock markets are informationally inefficient most of the time, whilst investorsâ irrationality may be more pronounced during certain periods. Furthermore, investorsâ behaviour in the cryptocurrency and emerging stock markets is more consistent with the adaptive market hypothesis than the efficient market hypothesis. This research suggests that cryptocurrency and emerging stock market investors should actively manage investment portfolios. Policymakers should be more concerned about information accessibility and quality, especially in the case of small-cap investment assets. JEL codes: G14;G15;G41
Layer-2 protocols can assist Ethereum's limited throughput, but globally broadcasting layer-2 data limits their scalability. The Danksharding evolution of Ethereum aims to support the selective distribution of layer-2 data, whose availability in the network is verified using randomized data availability sampling (DAS). Integrating DAS into Ethereum's consensus process is challenging, as pieces of layer-2 data must be disseminated and sampled within four seconds of the beginning of each consensus slot. No existing solution can support dissemination and sampling under such strict time bounds. We propose PANDAS, a practical approach to integrate DAS with Ethereum under Danksharding's requirements without modifying its protocols for consensus and node discovery. PANDAS disseminates layer-2 data and samples its availability using lightweight, direct exchanges. Its design accounts for message loss, node failures, and unresponsive participants while anticipating the need to scale out the Ethereum network. Our evaluation of PANDAS's prototype in a 1,000-node cluster and simulations for up to 20,000 peers shows that it allows layer-2 data dissemination and sampling under planetary-scale latencies within the 4-second deadline.
Sistem pemilihan konvensional masih menghadapi berbagai tantangan, seperti kurangnya transparansi, lambatnya proses penghitungan suara, dan birokrasi yang kompleks. Untuk menjawab permasalahan tersebut, penelitian ini mengembangkan sistem e-voting berbasis Blockchain dengan memanfaatkan platform ZenChain Testnet. Pemilihan ZenChain didasarkan pada kemampuannya menyediakan lingkungan uji coba yang ringan, mendukung smart contract, dan kompatibel dengan arsitektur DAO (Decentralized Autonomous Organizations). Metode yang digunakan adalah metode Prototype, yang diterapkan melalui tahapan desain awal sistem, implementasi fungsional, pengujian antarmuka, serta evaluasi berulang berdasarkan umpan balik. Tujuan dari penelitian ini adalah menciptakan sistem e-voting yang aman, efisien, dan transparan melalui pemanfaatan smart contract untuk mengotomatisasi proses validasi pemilih, pemungutan suara, dan perhitungan hasil. Hasil implementasi menunjukkan bahwa seluruh transaksi berhasil dicatat secara permanen di dalam Blockchain, sehingga mencegah potensi manipulasi data. Pengujian dilakukan menggunakan metode black box dengan 4 skenario uji fitur utama, seperti validasi pemilih, input suara, dan kalkulasi hasil. Semua skenario berhasil dijalankan tanpa error, dengan response time rata-rata di bawah 9 detik. Kesimpulannya, sistem e-voting berbasis Blockchain pada ZenChain Testnet terbukti mampu meningkatkan integritas dan efisiensi pemilu secara teknis. Namun, untuk implementasi skala besar, diperlukan strategi peningkatan infrastruktur, pengujian skalabilitas lebih lanjut, serta program literasi digital bagi masyarakat guna memastikan adopsi yang menyeluruh.
Giovanni De Gasperis, Sante Dino Facchini, Asif Saeed
In recent years, numerous regions worldwide have experienced devastating natural disasters, leading to significant structural damage to buildings and loss of human lives. The reconstruction process highlights the need for a reliable method to document and track the maintenance history of buildings. This paper introduces a novel approach for managing and monitoring restoring interventions using a secure and transparent digital framework. We will also present an application aimed at improving building structures with respect to earthquake resistance. The proposed system, referred as the âBuilding Ledger Dossierâ, leverages a Digital Twin approach applied to blockchain to establish an immutable record of all structural interventions. The framework models buildings using OpenSees, while all maintenance, repair activities, and documents are registered as Non-Fungible Tokens on a blockchain network, ensuring timestamping, transparency, and accountability. A Decentralized Autonomous Organization oversees identity management and work validation, enhancing security and efficiency in building restoration efforts. This approach provides a scalable and globally applicable solution for improving both ante-disaster monitoring and post-disaster reconstruction, ensuring a comprehensive, verifiable history of structural interventions and fostering trust among stakeholders. The proposed method is also applicable to other types of processes that require the aforementioned properties for document monitoring, such as the life-cycle management of tax credits and operations in the financial or banking sectors.
This study investigates the global adoption of Bitcoin by analyzing its price elasticity of demand (PED) across 46 countries or regions, with a focus on the interplay between economic, regulatory, and technological factors. Utilizing robust econometric techniques, including Huber regression, the research identifies significant variations in Bitcoin demand elasticity between developed and developing economies. The findings reveal that developed economies exhibit a mix of elastic and inelastic demand, driven by market maturity and discretionary consumption, while developing economies predominantly demonstrate inelastic demand, reflecting necessity-driven adoption amidst economic constraints. Key determinants of adoption include regulatory frameworks, such as legality, taxation, and anti-money laundering measures, alongside technological readiness indicators like blockchain infrastructure and internet penetration. These results underscore the critical influence of non-price factors on Bitcoinâs adoption dynamics and provide valuable insights for policymakers, investors, and industry stakeholders aiming to balance innovation with market stability. By offering a nuanced understanding of Bitcoin demand, this research contributes to the broader discourse on cryptocurrency adoption and its socioeconomic implications.
Software-Defined Networking (SDN) has revolutionized network administration with its unparalleled flexibility and programmability. The secure placement of controllers within a Software-Defined Networking framework remains a significant challenge. This research provides an innovative solution that integrates blockchain technology with the advanced reinforcement learning algorithm MuZero to optimize and secure the placement of SDN controllers. The suggested framework utilizes critical security parameters, including network latency, traffic volume, and the quantity of connected devices, to evaluate and record secure controller locations, employing Mininet for network emulation and OpenDaylight as the SDN controller. These criteria are essential for identifying secure deployment locations and assessing network efficacy. The suggested solution ensures the security and efficacy of controller placement in SDN systems by integrating blockchain technology for transparent and invulnerable documentation of secure locations.
The rapid adoption of Internet of Things (IoT) devices has significantly increased cybersecurity risks, making them vulnerable to anomalies, attacks, and unauthorized access. Traditional security mechanisms struggle to handle the massive data flow, real-time processing requirements, and evolving cyber threats in IoT networks. This paper presents an integrated approach using Deep Neural Networks and Blockchain technology (DNNs-BCT) to enhance anomaly detection and prevention in IoT environments. Our proposed framework employs DNNs for intelligent anomaly detection, leveraging multi-layer feature extraction and adaptive learning mechanisms. The DNN model is trained on IoT traffic datasets to classify network behavior as normal or anomalous, effectively detecting threats such as Distributed Denial of Service (DDoS) attacks, malware injections, and insider threats. Unlike traditional rule-based intrusion detection systems (IDS), the DNN continuously learns and adapts to new attack patterns, improving detection accuracy and false-positive reduction. This study integrates Blockchain technology into the IoT ecosystem to ensure data integrity, transparency, and decentralized security. Each IoT device logs its activity onto a private blockchain network, preventing data tampering, unauthorized access, and single points of failure. The blockchain employs smart contracts for automated threat response, instantly mitigating malicious activity without human intervention. This distributed ledger approach enhances trust, authentication, and secure communication across IoT devices. The synergy between DNN-based anomaly detection and Blockchain-based security provides a robust, scalable, and adaptive solution for real-time cybersecurity threats in IoT networks. With a low false-positive rate of 15.42% and a strong detection accuracy of 99.18%, the proposed model successfully identifies malicious activity, including malware injections and Distributed Denial of Service (DDoS) assaults. Blockchain technology replaces single points of failure and forbids illegal changes by providing data integrity, openness, and decentralizing powers. Furthermore, smart contracts allow autonomous, real-time attack responses, enhancing reaction time efficiency (95.25%) and general system scalability (94.96%).
This paper examines the strategic decisions of fashion brands to develop and sell non-fungible tokens (NFTs) within the metaverse. We construct two operational models based on whether NFTs are adopted: the traditional fashion model without NFT (Scenario T) and the digital fashion model with NFT (Scenario D). By comparing the equilibrium outcomes of fashion brands in Scenarios T and D, we derive valuable insights into the implementation of digital strategies in the fashion industry. Our analysis reveals three key findings. First and foremost, the proportion of fashion customers to conventional customers, as well as the digital value and cost of NFTs, are direct factors influencing the adoption of digital strategies by fashion brands. Secondly, opportunistic pricing by manufacturers is an indirect factor influencing fashion brandsâ strategic choices, and a fixed production price contract can effectively avoid this case. Finally, we find that personalized pricing and a free NFT strategy are effective tools to boost fashion brandsâ digital revenues.
Open access
Consumer Retail Behavior Studies
Consumer Behavior in Brand Consumption and Identification
Purpose: This article proposes and applies the 6V Framework to conceptualize and evaluate next-generation marketing channels in the digital economy.It aims to understand how emerging formats-such as voice commerce, immersive AR/VR environments, retail media networks, and Web3-based platforms-are reshaping customer engagement, brand experience, and value creation.Design/Methodology/Approach: Building on an extensive literature review and theoretical synthesis, the paper introduces the 6V Framework, consisting of six analytical dimensions: Value, Velocity, Visibility, Verifiability, Virtuality, and Vulnerability.The framework is applied to an in-depth case study of Nike .Swoosh, supported by a comparative evaluation of other leading platforms (e.g., Adidas, Gucci, Starbucks) to illustrate strategic patterns and innovation trajectories.Practical Implication: The article provides marketers, strategists, and digital transformation leaders with a practical framework for analyzing, designing, and governing complex marketing environments.It supports decision-making regarding channel investments, user experience design, and ethical risk management in data-rich, technology-driven contexts.Originality/Value: In contrast to legacy models focused on linear transactions and control, the 6V Framework captures the dynamic, participatory, and decentralized nature of modern marketing channels.It offers a novel conceptual lens for assessing strategic and operational implications of digital channel innovation.
Blockchain has become a cornerstone of trustworthy, decentralised information governance. Consensus protocols and cryptographic linkages guarantee data integrity, immutability, and verifiable provenance, eliminating reliance on a single trusted authority and mitigating data fragmentation. Within smartâhealthcare ecosystems, these capabilities enable the shift from siloed, centralised repositories to distributed, patientâcentric infrastructures. Because clinical data are highly sensitive and strictly regulated, robust assurances of integrity, confidentiality, and fineâgrained authorisation are essential. Integrating blockchain and smart contracts with technologies such as distributed offâchain storage and the Internet of Medical Things (IoMT) creates a resilient, scalable, and interoperable foundation for nextâgeneration healthcare data management. This research introduces hChain, a fourâgeneration family of distributedâledger frameworks that progressively strengthen security, intelligence, and scalability in smartâhealthcare environments. hChain 1.0 lays the groundwork with a blockchain architecture that safeguards patient data, supports realâtime clinical telemetry, and enables seamless interâinstitutional exchange. Building on this foundation, hChain 2.0 integrates InterPlanetary File System (IPFS) storage and smartâcontract enforcement to deliver tamperâproof, fineâgrained access control. hChain 3.0 embeds onâchain deepâlearning analytics, providing proactive, automated decision support across the care continuum while preserving data integrity. Finally, hChain 4.0 introduces a highly scalable, permissioned ledger augmented by an AttributeâBased Access Control (ABAC) layer, ensuring dynamic, contextâaware authorisation in complex organisational settings. The results demonstrate practical solutions for transforming data infrastructures from centralised to decentralised architectures, providing techniques that facilitate seamless integration with existing systems while enhancing blockchain scalability and privacy.
This research analyzes the impact of blockchain technology in the field of electronic evidence. It starts from a hypothesis of assuming that blockchain technology will have a significant impact on both public administrations and society in general, which will mean changing the way personal electronic information is managed by putting control in the hands of individual citizens rather than centralized servers or platforms. The article also analyzes regulatory efforts in the European Union to adapt to the changing landscape of electronic evidence, including the proposed eIDAS 2 regulation, which seeks to establish autonomous digital identities based on blockchain technology and then focuses on the procedural treatment of blockchain as a means and source of evidence and differentiates between this technology as a means of storing electronic evidence and as a mechanism to preserve and secure this type of evidence. Likewise, the text concludes by emphasizing the potential of blockchain technology in the context of Web3, where decentralized and interoperable systems are expected to play a fundamental role in the Spanish and European administration of justice.
For any meaningful instructional delivery to take place, the teacher must clearly understand who the learners are: their strengths, weaknesses, environment, the goal of instruction, the pace to mention but a few.This process is better referred to as instructional analysis.This paper posits that instructional analysis, the foundational phase of instructional design, serves as the indispensable basis for achieving high-quality and impactful instructional delivery.It explores the multifaceted components of instructional analysis, including learner analysis, context analysis, content/task analysis, and performance analysis, demonstrating how insights derived from these processes directly inform strategic decisions regarding instructional strategies, media selection, and assessment design.Drawing upon established instructional design models and contemporary research, this paper highlights the benefits of thorough instructional analysis in optimizing learning outcomes, enhancing engagement, and ensuring the relevance and efficiency of educational interventions.It also addresses practical challenges in conducting instructional analysis, offering considerations for educators and designers in diverse learning environments, particularly within the evolving nature of education in the 21st century.
This paper proposes DeFiDonate, a web-based decentralized application that facilitates the transparency and privacy of donations to charities while also increasing trust through the use of blockchain and various Decentralized Finance (DeFi) solutions. The problem with traditional donation models is that they lack traceability and are centralized, with limitations and restrictions on donors. DeFiDonate proposed using Elliptic Curve Cryptography (ECC), Non-Fungible Tokens (NFTs), a form of smart contracts, and Elliptic Curve Digital Signature Algorithm (ECDSA).DeFiDonate provides flexibility for donors by making either a direct donation to beneficiaries or donating to a liquidity pool, then distributing funds through a decentralized voting system and encrypting sensitive data, like the wallet addresses, donation amounts, and donors' NFT identifiers with ECC, meaning it's recorded in both on-chain and off-chain safety, and confirming the transactions and validating the signature is unauthentic with ECDSA, the implementation of DeFiDonate composed on Django and Solidity for the creation of smart contracts; Truffle, Ganache, and MetaMask for local testing. These results indicate that the system is safe for use, as it provides transaction integrity and information security. Based on performance analysis carried out in Truffle Develop, the use of NFTs within contracts was found to be associated with a notable decrease in execution time. Another application blockchain developers can discuss is DeFiDonate, which exemplifies a trusted, decentralized, and transparent method of digital giving.
Alternative Assets tokenization is transforming non-traditional financial instruments are represented and traded on the web. However, ensuring trustworthiness in web-based tokenized ecosystems poses significant challenges, from verifying off-chain asset data to enforcing regulatory compliance. This paper proposes an AI-governed agent architecture that integrates intelligent agents with blockchain to achieve web-trustworthy tokenization of alternative assets. In the proposed architecture, autonomous agents orchestrate the tokenization process (asset verification, valuation, compliance checking, and lifecycle management), while an AI-driven governance layer monitors agent behavior and enforces trust through adaptive policies and cryptoeconomic incentives. We demonstrate that this approach enhances transparency, security, and compliance in asset tokenization, addressing key concerns around data authenticity and fraud. A case study on tokenizing real estate assets illustrates how the architecture mitigates risks (e.g., fraudulent listings and money laundering) through real-time AI anomaly detection and on-chain enforcement. Our evaluation and analysis suggest that combining AI governance with multi-agent systems and blockchain can significantly bolster trust in tokenized asset ecosystems. This work offers a novel framework for trustworthy asset tokenization on the web and provides insights for practitioners aiming to deploy secure, compliant tokenization platforms.
Research background: In todayâs digital age, traditional environmental, social, and governance (ESG) development paths are gradually facing challenges, including from digital technologies. In particular, the potential roles of artificial intelligence (AI), cloud computing (CC), and blockchain (BC) in the ESG market have not been fully explored. Purpose of the article: This study explores the deep integration of digital technology and ESG by evaluating the correlation and spillover effects among AI, CC, BC, and eight global ESG indices. Methods: This study explores the spillovers between AI, CC, BC, and eight global ESG indices by cross-quantilogram and quantile time-frequency connectedness approaches. Findings & value addition: The lower quantile of ESG returns has a weak positive (strong negative) correlation with the lower (upper) quantile of digital technology. Next, the spillover effects vary with time, frequency, and quantile levels. Meanwhile, the North America and Asia-Pacific developed ESG indices serve as the transmitter and receiver of spillover effects, respectively. Furthermore, the dependence between digital technology and ESG returns is insignificant before the COVID-19 crisis but increases after it. This quantile-dependent asymmetry fundamentally challenges linear assumptions prevalent in current ESG-technology integration theories. Overall, this study contributes by integrating AI, CC, BC, and ESG into a unified framework, and analyzing their interaction mechanisms. Furthermore, it dynamically analyzes the asymmetry over long and short-term horizons, and highlights the hedging role of digital technology in stabilizing ESG markets. Moreover, we provide novel insights about the interconnectedness between these markets, offering valuable guidance on risk management. Consequently, regulators should urgently explore the development of digital asset-based ESG derivatives as targeted risk mitigation tools. Positioned at the cutting-edge, this work sets a methodological benchmark for analyzing non-linear, frequency-sensitive interdependencies within the rapidly evolving ESG-digital nexus, transforming the theoretical framework from static linearities to dynamic non-linearities. Finally, this study proposes some reasonable suggestions, including raising risk awareness, promoting digital transformation, building integration and innovation platforms, and leveraging ESGâs diffusion role.
In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem.
The recent blockchain policy pronouncement in Nigeria gave rise to examine the effect on digital currency market performance. Knowing that policy statements played a dynamic role on market performances, and for the fact that digital currency is link to market volatility, this work analyzed the impact of Nigerian blockchain policy on digital currency market performance during the short-term periods. Based on the exigencies, the research covered a period of 23 weeks using a-weekly data between May 3, 2023 and October 4, 2023. The study employed selected top-five digital currencies including Bitcoin, Ethereum, Tether, BNB, and XRP of their market performances extracted from crypto database. The generalized autoregressive conditional heteroskedasticity (GARCH) least squares analytical tool was applied to ascertain how Bitcoin, Ethereum, Tether, BNB, and XRP digital currencies market performance responded to Nigerian blockchain policy in the short-term. The findings showed that Nigerian blockchain policy impacted negatively on Bitcoin, XRP, and BNB market performance in the short-term. However, Nigerian blockchain policy impacted positively on Tether, and Ethereum market performance in the short-term. The research further revealed Ethereum, and BNB digital currencies constituted significant variables of study. Finally, Nigerian blockchain policymakers were recommended to revised and address the diverse impacts on digital currencies with tailored regulations to enhance investors protection, and support the positive trends for a balance-support of the digital currency market.
Hameer Hussain Shah, Asra Shaikh, Muhammad Mujtaba, Tanveer Hussain Shah
Purpose: The purpose of this study is to examine how the adoption of major cryptocurrencies affects the financial markets of Pakistan. It focuses on three key areas: the gold market, the stock market (KSE-100 index), and the bond market. Design: This research employs a quantitative approach, utilizing regression analysis to investigate the relationship between the adoption of five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple) and their impact on gold prices, stock market performance, and bond price movements in Pakistan. Findings: The results show that cryptocurrencies do affect financial markets, but each coin has a different impact. Bitcoin and Binance Coin increase gold prices, while Cardano and Ripple decrease them. In the stock market, Bitcoin lowers the index, but Ethereum and Ripple increase it. Cardano and Binance Coin have little to no impact on stocks. For bonds, Cardano and Ripple lower prices, while Binance Coin and Ethereum raise them. Bitcoin has a small negative effect on bonds. Originality and Value: This study contribute significantly to reshaping the investment culture in Pakistan, particularly in the context of evolving regulatory frameworks, creating awareness about digital currencies especially cryptocurrencies so that financial investors, portfolio and fund managers can take better informed decisions. Keywords: Cryptocurrency, Blockchain Technology, Financial Inclusion, Bitcoin, Ethereum, Gold Market, Stock Market, and Bond Market JEL Classification codes: G0, G1, G2, O3
Cryptocurrencies are one of the new financial assets that might provide some hedge, safe havens and diversification benefits towards traditional financial assets.However, the impact of COVID-19 towards their properties was also acknowledged in the literature and showed that COVID-19 significantly changed their properties against other financial assets.However, the comparison of the co-movement for the cryptocurrency and financial assets in the three different periods (pre-COVID-19, during COVID-19, and post-COVID-19) is relatively limited.Therefore, this study aimed to study the differences in the co-movement between Bitcoin and stock indices in ASEAN-5 markets in these three periods.The study period spanned from early January 2018 until the end of June 2024, and the conditional correlation was obtained through the MGARCH-DCC approach.These conditional correlation series were then divided into three periods, and statistically compared their statistical differences using an independent t-test.The results found that the comovement between Bitcoin and market indices was significantly different between pre-COVID-19 and during COVID-19 in all ASEAN-5 markets.Besides, when comparing pre-COVID-19 and post-COVID-19, the result showed that the co-movement between Bitcoin and market indices in Malaysia and Thailand was significantly reduced, while significantly enhanced between Bitcoin and market indices in Indonesia and the Philippines markets.Moreover, the results further revealed the significant differences between the co-movement of Bitcoin and market indices in Malaysia, Singapore and Thailand markets.Some useful implications were obtained from the study's findings, and it is expected to be beneficial to the literature and also to stakeholders.