Decentralized finance introduces new business models and use cases as part of digital finance. Restaking has recently emerged as a transformative mechanism in DeFi, promising extra yields but introducing complex and interconnected risks. The paper monitors the current restaking landscape, empirically analyzes the revenue drivers of a liquid restaking protocol, and conducts a technical investigation on the emitted risk arising from the interconnection between liquid restaking and other protocols. The revenue dynamics of Renzo Protocol are analyzed by employing an OLS regression model, Granger-causality and random forest feature importance tests. Our results identify that revenue is primarily predicted by the value locked in the underlying EigenLayer ecosystem, the yield of Renzo protocol's liquid restaking token and the multi-blockchain expansion of that token. The multi-blockchain expansion of the liquid restaking token presents a double-edged sword: bridging to other networks is crucial for user adoption, but it adds the bridge risks to the existing risks of restaking. We investigate the cross-contamination risk between different DeFi services and the liquid restaking protocol. By mapping the asset flow across the decentralized finance ecosystem, it is detected that the bridge risk of the current size of Renzo's liquid-restaking assets does not impose a systemic risk on the current restaking and staking ecosystem. To address the potential consequences of the emphasized interconnection risks, we introduce two hypothetical scenarios and a stress test, assuming a large number of compromised liquid restaking tokens and a smart contract logic failure in a DeFi protocol. Considering the overall liquid-restaking protocols and the growing interconnection, this analysis requires further work to explore the growing complexities.
The decentralisation of authority and automated trust are the main reasons blockchain receives widespread praise. Token-based governance systems tend to maintain centralised control because early adopters and institutional stakeholders maintain most of the influence. Blockchain governance presents itself as an ethical and institutional problem instead of a technical issue. The paper uses deliberative democracy and democratic innovation theory to demonstrate that decentralised systems need to establish legitimacy through inclusive processes that combine reason and participation. The analysis evaluates Proof-of-Stake and DAOs as dominant governance models because they contain structural barriers and procedural weaknesses. The paper introduces design interventions such as sortition and quadratic voting, participatory panels and modular deliberation layers as potential solutions to embed democratic legitimacy into blockchain infrastructure. Blockchain technology enables the creation of new institutional frameworks which base their operations on democratic principles. The paper establishes that future governance needs to combine contestation and collective reasoning with consensus and coordination.
We analyze the market quality of centralized crypto exchanges and decentralized blockchain-based venues (DEXs) using a unique and comprehensive data set. Focusing on two fundamental aspects, transaction costs and deviations from the no-arbitrage condition, we estimate the causal effect of gas fees on DEX market quality. We show that these fixed costs impose a significant burden on relatively small trades and cause persistent arbitrage deviations. Conversely, DEXs offer more competitive transaction costs for larger trades, offering a more favorable environment for institutional investors. Furthermore, we provide causal evidence that innovations aimed at enhancing the flexibility of liquidity provision in DEX markets lead to sizeable improvements in market quality. This paper was accepted by Agostino Capponi, finance. Funding: A. Ranaldo acknowledges financial support from the Swiss National Science Foundation nccrâon the move [Grant 204721]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07703 .
This paper emphasizes the critical role of interoperability in enabling efficient and secure communication for the fragmented distributed ledger ecosystem, particularly within on-chain finance. The purpose of this study is to streamline and accelerate empirical research on the intersection of cross-chain interoperability solutions and their impact within on-chain finance. The analysis examines the relationship between financial use and interoperability while comparing the properties of novel cross-chain interoperability protocols (LayerZero, Wormhole, Connext, Chainlink Cross-Chain Interoperability Protocol, Circle Cross-chain Transfer Protocol, Hop Protocol, Across, Polkadot, and Cosmos), focusing on their design, mechanisms, consensus, and limitations. To encourage further empirical study, the paper proposes a set of network metrics and sample statistical models and provides a framework for evaluating the performance and financial implications of interoperability solutions.
Md. Abu Issa Gazi, Sofiane Laradi, Amina Elfekair, Afaf Ahmed ¡ 6 authors
Understanding usersâ continued usage beyond initial adoption is fundamental to the long-term success of any technology. Notwithstanding the growth of cryptocurrency usage, studies have primarily examined factors explaining use intention (pre-adoption), whereas understanding continued use remains limited (post-adoption). Consequently, this study aims to examine continuance intentions to use cryptocurrency among Malaysians by employing the Unified Theory of Acceptance and Use of Technology (UTAUT), integrating attitudes, trust, and technology readiness. Using a quantitative approach based on self-reported data collected via snowball sampling, structural equation modeling (SEM) analysis reveals that the determinants of UTAUT are positively associated with attitudes toward and trust in cryptocurrency, except for the association between effort expectancy and attitudes. Additionally, trust, attitudes, and technology readiness significantly influence continuance intention, accounting for 61% of its variance. This study makes modest theoretical contributions to the technology adoption literature by shifting the focus to cryptocurrency post-adoption (i.e., why people continue to use cryptocurrency), theorizing mechanisms linking attitude and trust within the UTAUT, and examining the role of technology readiness in predicting cryptocurrency adoption. This study provides actionable recommendations for cryptocurrency providers and policymakers to nurture sustained use of decentralized digital currencies.
The emergence of decentralized finance (DeFi) has prompted a new, highly interwoven financial system in which the stability of the financial system is fundamentally dependent upon the existence of digital assets, in particular stablecoins, that serve as both a method of conducting transactions, collateral, and a source of liquidity. Although DeFi is said to be efficient, programmable, and disintermediated, the structural complexity and composability of the DeFi system also create new systemic- risk channels that are similar to the impact of fragilities in conventional finance (Auer et al., 2024; Xu et al., 2024). The role of stablecoins in this architecture is to facilitate trading, leverage, and settlement of protocols, though the design and collateralization process puts them at risk of derailing the stablecoin and liquidity shocks and runs (Catalini et al., 2022; Hoang and Baur, 2024). These dynamics are similar to traditional bank run and liquidity crisis theories, in which the lack of coordination and redemption could cause damaging withdrawal effects (Diamond and Dybvig, 1983; Bernardo and Welch, 2004). In the case of the elements of DeFi, the volatility can spread very quickly between lending pools, automated market makers, and cross-chain bridges, facilitating the transfer of stress and volatility across platforms and asset classes (Zieba et al., 2019; Pagnottoni, 2023). The lack of centralized backstops, along with the algorithmic governance and large leverage, also serves to further enhance the risk of local perturbations developing into system-wide contagion. Such vulnerabilities have increased the arguments for risk-sensitive system design, greater transparency, and regulatory coordination to reduce spillovers to the financial system more generally (FSB, 2018; Manaa et al., 2021; Fantacci and Gobbi, 2024). Altogether, the discussion shows that the concept of stablecoins is an important crossroads in the stability environment of DeFi: not only do they allow markets to operate, but also they are a primary medium through which runs and shocks are propagated. The knowledge of these mechanisms is paramount in the formation of the resilient protocol design, supervisory systems, and eventual research on systemic risk of programmable financial systems.
The centralization of digital content creation and credentialing platforms has resulted in opaque monetization structures, monopolistic data silos, and a persistent absence of verifiable user sovereignty over intellectual contributions. This paper introduces Metaplay, a decentralized content marketplace architecture engineered to disintermediate the content creation and talent development lifecycle. Leveraging a modular blockchain framework, Metaplay utilizes Zero-Knowledge Rollups (zkEVM) for high-throughput, low-latency execution, and EIP-4844 blob-carrying transactions to minimize data availability costs. We introduce a privacy-preserving credentialing mechanism utilizing Soulbound Tokens (SBTs) and zk-SNARKs, enabling non-transferable, cryptographically verifiable proof of skill acquisition without compromising user privacy. Platform moderation employs a Decentralized Autonomous Organization with Identity-Gated Quadratic Voting to mitigate plutocratic governance capture. A dual-token incentive model (PLAY utility token and CRED reputation token) aligns creator economic incentives with verifiable content quality. Comparative benchmarks demonstrate transaction cost reductions exceeding 95% relative to Ethereum Layer-1 baselines.
Decentralised Finance (DeFi) applications involve a large volume of funds and exhibit diverse user behaviours, including malicious activities such as smart contract exploits and financial scams. Existing approaches struggle to capture complex behaviours. To address this gap, we propose a general Blockchain User Behaviour Analysis (BUBA) pipeline for DeFi security. The pipeline presents an automated action formation process that takes blockchain transactions as inputs and outputs user actions. In addition, BUBA introduces a dual Graph Neural Network (GNN) model that jointly captures user action features, contract and token interactions, and heterogeneous graph structure information to produce rich behavioural embeddings, enabling effective clustering of semantically meaningful user behaviours. We evaluate the proposed pipeline on Uniswap V3, where it outperforms baseline methods in identifying and differentiating suspicious behaviours. A further case study on Sushiswap V2 demonstrates the generalisability of the pipeline across DeFi applications.
Jiahao Pei, Ning Duan, Gang Du, Kejia Zhang ¡ 5 authors
Smart contracts are self-executing programs running on blockchain networks. Once deployed, they are immutable, making their security critically important. Reentrancy vulnerability is one of the most notorious security vulnerabilities in smart contracts, which allows attackers to repeatedly invoke target functions before the execution of contract functions is completed, thereby stealing funds or corrupting contract states, resulting in severe economic losses in recent years. Existing detection tools often suffer from insufficient path coverage and oversimplified detection rules. This paper proposes a static analysis approach based on smart contract bytecode that recovers execution paths by constructing a control flow graph (CFG), identifies all potential vulnerability paths using taint analysis, and detects reentrancy vulnerabilities through path matching rules. To validate the approachâs effectiveness, we compare it with mainstream detection tools on an annotated smart contract dataset. Experimental results demonstrate that the approach achieves a precision of 93.2%, outperforming other tools overall. Additionally, through analysis of 2023 real-world smart contracts deployed on Ethereum, 21 contracts are found to contain reentrancy vulnerabilities.
Minela NuhiÄ-MeĹĄkoviÄ, M. Kabir Hassan, Admir MeĹĄkoviÄ
Purpose This study aims to systematically synthesize academic literature on Islamic FinTech published prior to 2025 to identify prevailing themes, regional and methodological trends and unresolved research gaps. Design/methodology/approach A systematic literature review (SLR) was conducted following the PRISMA 2020 protocol to ensure transparency and replicability. A total of 162 peer-reviewed journal articles were identified from Scopus and Web of Science databases using defined keywords. Bibliometric mapping (via VOSviewer), qualitative coding and descriptive statistics were used to identify major themes, methodological patterns and research gaps. Findings The review reveals a rapid increase in Islamic FinTech scholarship, particularly after 2020, with Southeast Asia dominating the output. Five major thematic clusters emerge: digital transformation, technology adoption, Shariah compliance, decentralized finance and Islamic social finance. Research limitations/implications Findings point to the importance of more diversified methodologies, cross-regional studies, harmonized Shariah standards and inclusive digital financial solutions. Practical implications The findings suggest that effective adoption of FinTech can enhance cost efficiency, operational scalability and product diversification for Islamic financial institutions. Social implications Islamic FinTech can widen social inclusion, improve transparency and support social goals. To unlock that potential, the study needs shared Shariah and regulatory standards, user-centred design and pilot projects that measure outcomes. Originality/value To the best of the authorsâ knowledge, this is the first comprehensive SLR of Islamic FinTech integrating Scopus and Web of Science sources within the PRISMA 2020 framework, providing a consolidated foundation for future empirical, theoretical and policy research.
Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks. In this paper, we present TOKENSCOUT, the first temporal GNN-based framework for scam token early detection. TOKEN SCOUT formulates token transfer data as a dynamic temporal attributed multigraph and leverages the temporal graph learning model to learn graph representations. It also builds a graph rep resentation refining model based on contrastive learning to learn a more discriminative representation space for risk identification. We evaluated TOKENSCOUT using a comprehensive dataset of 214,084 standard ERC20 tokens from 2015 to February 2023. TOKENSCOUT achieves a balanced accuracy of 98.41%. Additionally, from March to May 2023, deploying TOKENSCOUT on Ethereum effectively identified 706 rugpulls, 174 honeypots, and 90 Ponzi schemes, thereby alerting to potential risks exceeding $240 million.
The rapid expansion of digital banking, mobile payments, decentralized finance, and cross-border electronic transactions has fundamentally transformed global financial ecosystems while intensifying exposure to sophisticated cyber threats, fraud networks, synthetic identity schemes, and money laundering operations. Conventional rule-based security infrastructures lack the adaptability required to counter dynamic and large-scale financial crimes. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative enablers of intelligent financial security, supporting real-time fraud detection, behavioral authentication, transaction risk scoring, and regulatory compliance automation. This chapter presents a comprehensive examination of advanced machine learning techniquesâincluding deep learning, graph neural networks, anomaly detection models, and reinforcement learningâfor securing digital transactions and identifying coordinated fraud rings within complex financial networks. Integration of AI with blockchain consensus mechanisms, cryptographic infrastructures, and Regulatory Technology (RegTech) platforms is analyzed to demonstrate how adaptive intelligence enhances network resilience, transparency, and operational efficiency. Emphasis is placed on explainable and fairness-aware AI frameworks to ensure ethical accountability, regulatory alignment, and bias mitigation in automated financial decision systems. Privacy-preserving approaches such as federated learning and secure multi-party computation are also explored to address data governance constraints in cross-institutional collaboration. The chapter consolidates emerging research directions, identifies persistent technical and ethical challenges, and proposes an integrated AI-driven security architecture for scalable and trustworthy digital financial ecosystems.
The rapid digitalization of financial ecosystems has transformed online payments, transaction processing, and investment management into highly interconnected, data-intensive infrastructures. This transformation has simultaneously expanded exposure to cyber fraud, money laundering, identity theft, and market volatility, necessitating intelligent and adaptive security mechanisms. Advanced artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and graph-based analytics, have emerged as critical enablers of secure payment processing, real-time fraud detection, and predictive financial forecasting. Intelligent architectures embedded within online payment systems facilitate dynamic risk scoring, anomaly detection, behavioral profiling, and automated decision-making under strict latency constraints. This chapter presents a comprehensive examination of intelligent system frameworks for digital finance, integrating scalable cloud-based deployment, blockchain-enabled transaction integrity, explainable AI for regulatory compliance, and synthetic data generation for fraud simulation. Reinforcement learning approaches for portfolio optimization and risk-aware forecasting are analyzed to highlight adaptive investment strategies in volatile markets. Emphasis is placed on addressing class imbalance, adversarial threats, model interpretability, privacy preservation, and governance challenges within automated financial infrastructures. Emerging research directions such as federated learning, decentralized finance intelligence, and AI-driven anti-money laundering systems are also discussed to outline future technological trajectories. The presented synthesis establishes a structured foundation for developing secure, transparent, and scalable intelligent financial ecosystems aligned with regulatory and operational requirements of modern digital economies.
Abstract Purpose â The study evaluates the connectedness among the less riskier Digital Assets by investigating the functions of gold backed cryptocurrency alongwith Fan Tokens, Non-fungible Tokens and Real estate tokens, as a new alternative asset class that can be utilized by portfolio managers and investors alongwith policy makers. Design/methodology/approach â This study uses Quantile Vector Auto Regression analysis to measure the quantile cohesiveness among Islamic Cryptocurrencies, Non-Fungible Tokens, Fan Tokens and Real Estate Tokens, recommended by Ando et al., (2022) given extreme quantiles, which specify tail features among different markets performing under extreme conditions. The quantile cohesiveness proposed by Ando et al. (2022) is the blend of quantile vector auto regression with Diebold and Yilmaz (2012) methodology of spillovers for measuring the cohesiveness of the volatilities of markets for extreme higher (95th) and extreme lower (5th) quantiles. Findings â The findings of the QVAR divides spillover in two market condition i.e. median and extremes. In median market condition or we can say normal conditions findings of QVAR shows that Fan token is the major transmitter of shocks while Islamic crypto like X8X is major receiver of the shocks. In extreme condition the major transmitter remains the same i.e. Fan Tokens but major receiver of shock is Real Estate Tokens. Originality/ Value: â Study offer valuable insights to policy makers, portfolio managers and individual investors. For instance study enable the portfolio managers and investors to understand spillovers among Islamic Crypto, Non-Fungible Tokens, Fan Tokens and Real Estate tokens, which will help them in making suitable portfolios. By investigating the function of Islamic gold-backed cryptocurrencies as a new and alternative asset class that can be utilized by both portfolio managers and investors, looking to invest in Islamic Products, to lower their risk of investment. Research Implications: - This study brings novel insight for portfolio optimization and diversification. The findings of this study will have implications for global investor, researcher and policy makers.
This study identifies major approaches in token design for founders in the cryptocurrency/web3/blockchain space. The high failure rate of blockchain companies means that successful long-term performance will depend greatly on well-designed tokens. This study will integrate all prior research to highlight the most important aspects of structured tokenomics, including token utility, governance, and security. The study also contributes to the literature by introducing the Business Model Canvas as a conceptual framework that enables the integration of best practices for token design, drawing on both academic and industry literature. The results indicate significant gaps in the literature. This study offers new and practical insights for founders to enhance stakeholdersâ engagement, improve regulatory compliance, and ensure project viability in the volatile cryptocurrency market. Furthermore, this research generates new knowledge that bridges the gap between the theory and practice of tokenomics, laying the groundwork for future research to develop and refine token design strategies.
This paper explores how social influence and peer networks shape the adoption of cryptocurrencies and decentralized finance (DeFi) platforms. Drawing from qualitative interviews and social theory, the study examines how interpersonal communication, social media influence, and online communities impact user behavior. Findings reveal that peer endorsement and communal learning are strong drivers of trust and experimentation in the crypto space, especially in regions with limited institutional trust. Peer networks act as informal but powerful educational structures, providing newcomers with advice, emotional support, and real-time market insights. In many cases, peer encouragement is what propels hesitant individuals to take the first step toward using crypto wallets or engaging in DeFi protocols. However, the influence of peers can also perpetuate hype-driven narratives, misinformation, and herd behavior, leading to poor financial decisions or susceptibility to scams. The paper concludes with recommendations for leveraging peer networks in designing effective crypto awareness and onboarding strategies. These include integrating community leaders into education campaigns, offering platform incentives for verified peer mentorship, and collaborating with trusted influencers to communicate risks and best practices. Understanding the dynamics of social influence can help policymakers, educators, and platforms foster more ethical, inclusive, and informed crypto adoption pathways globally.
Using transaction cost economics (TCE) and agency theory, this paper examines how blockchain, smart contracts, and decentralized autonomous organizations (DAOs) reconfigure financial services across payments, wealth management, real estate, and corporate governance. Three research questions are addressed: (1) What are the quantifiable efficiency gains from blockchain-based real-time settlement compared with legacy systems? (2) How do blockchain technologies reduce intermediation and agency costs in wealth management and real estate? (3) Finally, to what extent do DAOs resolve or transform traditional corporate governance problems? By combining a present-value model calibrated to U.S. Automated Clearing House (ACH) data ($86.2 trillion in annual volume), comparative institutional analysis, and synthesis of empirical evidence from pilot implementations and on-chain governance metrics, this paper makes three principal contributions. First, real-time settlement yields approximately $12 billion in annual opportunity cost savings at the baseline 7.5% discount rate, with sensitivity analysis producing a range of $8â15 billion. The majority of gains accrue from moving to same-day or within-hour settlement. Second, tokenization and smart contract escrow substantially reduce real estate intermediation costs, blockchain-based digital identity streamlines wealth management onboarding, and a stablecoin taxonomy classifies fiat-collateralized, crypto-collateralized, and algorithmic designs by risk profile. Third, on-chain data reveal persistent governance token concentration (Gini > 0.98) and low voter participation (typically below 10%), exposing a gap between DAO theory and practice. Blockchain-specific risks are mapped to National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0, and mechanism design solutions, such as quadratic voting and AI-assisted proposal evaluation, are proposed to address whale dominance. Effective adoption requires hybrid architecture combining on-chain automation with off-chain structures for accountability and regulatory compliance.
Artificial intelligence (AI)-powered technology integration in social fintech has transformative potential to advance social responsibility and support sustainable development. This research examines a Blockchain-based lending mechanism that integrates centralized exchanges (CEX) and decentralized exchanges (DEX) to facilitate seamless financial transactions and equitable resource allocation. AI-driven tools are utilized to enhance transparency, accuracy, and security, while smart contracts facilitate the efficient management and verification of loan distribution. The proposed system focuses on helping underserved communities, poor regions, and green businesses, promoting fair and sustainable finance in line with the Sustainable Development Goals (SDGs). The hybrid ecosystem combines the liquidity and regulatory compliance of centralized exchanges with the autonomy and reduced intermediary involvement of decentralized exchanges. AI enhances loan processing, reducing biases and inefficiencies. This framework with smart contracts is to provide scalable, auditable lending aligned with sustainable goals. Machine Learning (ML) algorithms verified loan eligibility with the borrower dataset. The performance of Random Forest algorithms is good due to their robustness and ensemble learning features. Then, Optuna enhanced model tuning, and SHapley Additive exPlanations (SHAP) identified key parameters. Finally, Smart contracts ensured secure, autonomous execution of green loans based on ML verification and sustainability criteria.
This study determines whether Bitcoin enhances portfolio diversification and serves as a valuable investment asset during the COVID-19 crisis. In particular, we evaluate the significance and magnitude of the risk price associated with Bitcoinâs returns based on the ICAPM and NARDL models. Three methodological approaches were employed. First, we use the Intertemporal Capital Asset Pricing Model (ICAPM) to assess the effect of Bitcoin on a portfolio comprising 25 Fama-French portfolios. Second, a Nonlinear Autoregressive Distributed lag (NARDL) model explores Bitcoinâs impact on cross-sectional variation within the Fama-French portfolios, capturing potential asymmetric responses to price changes. Finally, we determine Bitcoinâs risk premium using the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model (FF3), and the Fama-French five-factor model (FF5). Bitcoin fails to provide significant diversification benefits for profitability factor (RMW), and exhibit insensitivity to value (HML) and investment (CMA). The NARDL model indicates a potential hedging role only during crypto market downturns. The factor models reveal that Bitcoin behaves differently than traditional assets, exhibiting low sensitivity to market risk and a negative relationship with the size premium, further supporting its potential for diversification within specific portfolio contexts. Our finding shows that Bitcoin can protect the 25 Fama-French portfolio when Bitcoin loses value.
The rapid expansion of Financial Technology (FinTech) is fundamentally reshaping financial systems, yet its role as a source of systemic risk and its dynamic connectedness with traditional energy and macroeconomic markets remain critically underexplored. This paper employs an integrated time-frequency framework to model financial spillover networks and demonstrates its utility in analyzing the connectedness between emerging FinTech sub-sectors, energy markets, and macroeconomic uncertainty. Using the Diebold and Yilmaz (2012) spillover index in the time domain and the BarunĂk and KĹehlĂk (2018) spectral decomposition in the frequency domain, we uncover a highly interconnected system: total connectedness reaches 43.62% for returns and 40.65% for volatility, showing that price shocks propagate more strongly than risk shocks. During the COVID-19 period, interconnectedness surged above 70%, highlighting how external shocks intensify contagion. We find that key FinTech indices such as Kensho Future Payments, KBW FinTech, and Kensho Alternative Finance act as major net transmitters, while the Distributed Ledger index, geopolitical risk, U.S. policy uncertainty, Brent oil, and U.S. 10-year Treasury yields are net receivers, signaling that within the financial network, shock propagation is now led by FinTech rather than emanating primarily from traditional macroeconomic indicators. Frequency results add important insight: volatility spillovers are mainly short-term (44.57%), reflecting transient fear contagion, while return spillovers are more persistent. Overall, our findings challenge the macro-driven spillover view and offer a time-sensitive framework for effective hedging and regulation. FinTech emerges as a key short-term shock transmitter, with clear implications for investorsâ hedging strategies and regulatorsâ systemic risk monitoring.
Mbonigaba Celestin, Jerryson Ameworgbe Gidisu, M. Vasuki & A. Dinesh Kumar
We examine how legal governance structures influence the reliability of blockchain based commercial transactions within emerging digital markets. We develop and empirically evaluate the Blockchain Legal Transaction Integrity Model using the Global Blockchain Regulation and Smart Contract Adoption Dataset covering the period 2020 to 2025 across major blockchain adopting jurisdictions including the United States, the United Kingdom, Singapore, Estonia, and Ghana. The model links regulatory clarity, compliance enforcement mechanisms, and legal recognition of smart contracts with commercial transaction integrity while accounting for institutional legal capacity as a conditioning factor. Quantitative analysis shows that stronger regulatory clarity, active enforcement supervision, and legally recognized smart contracts significantly improve transaction transparency, contract execution reliability, fraud reduction, and business trust in blockchain systems. Institutional legal capacity amplifies these effects by strengthening regulatory interpretation and dispute resolution capability. The results demonstrate that blockchain markets achieve reliable digital commerce not only through technological design but through coordinated legal governance structures. The findings advance institutional governance theory and provide policy guidance for regulators seeking to strengthen digital financial ecosystems and cross border blockchain commerce.
Alexander Kropiunig, Svetlana Kremer, Bernhard Haslhofer
Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.