Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare.
Open access
2 source records
Machine Learning in Healthcare
Explainable Artificial Intelligence (XAI)
Artificial Intelligence in Healthcare and Education
Financial transactions demand exceptionally robust security, especially in light of the rapid advancement of quantum computing, which poses a severe threat to classical cryptographic mechanisms used in modern banking systems. Among various financial operations, transaction processing remains the most critical and vulnerable component. To address this emerging challenge, we introduce a Distributed Ledger Technology (DLT)-based secure framework for quantum-resistant financial transactions. The proposed architecture leverages lattice-based cryptographic security to ensure resilience against quantum attacks while preserving essential security attributes such as privacy, accountability, and data integrity. Furthermore, to demonstrate its effectiveness, the proposed framework is also compared with existing solutions in the literature.
Cryptocurrency has been legalized in the United States. The 2025 GENIUS Act provides a sensible regulatory framework for cryptocurrency as a medium of exchange while avoiding predicted monetary chaos. The Act correctly distinguishes between volatile fiat cryptocurrencies like Bitcoin, which are unsuitable as payment mechanisms, and stablecoins backed by traditional currencies and reserves. Historical analysis spanning American monetary systems from the âfree banking eraâ before 1860 through modern digital payments demonstrates that private money has long coexisted with government currency but requires appropriate regulation to prevent bank runs, fraud, and monetary instability. The GENIUS Act addresses key cryptocurrency risksâtechnological failures, volatility, issuer fraud, and loss of monetary controlâthrough one-to-one reserve requirements, business activity restrictions, supervisory oversight, and priority for stablecoin holders in bankruptcy proceedings. Critics argue the Act enables monetary chaos, lacks consumer protections, and permits âBig Techâ to control money supply. These criticisms are largely unfounded. The Actâs reserve requirements prevent fractional banking and money supply disruption, while existing corporate governance and consumer protection mechanisms provide adequate safeguards. The GENIUS Act represents neither a return to nineteenth-century monetary chaos nor abandonment of oversight, but rather a measured approach distinguishing payment uses from investment uses of cryptocurrency. Success ultimately depends on whether stablecoins can satisfy the âno-questions-askedâ principle and offer competitive transaction costs compared to existing payment systems like credit cards and digital wallets.
Nandhini S, Hrithik M, Kamalesh S, Aswin C ¡ 6 authors
ABSTRACT: Centralized digital marketplaces dominate todayâs online commerce but suffer from inherent limitations such as single points of failure, lack of transparency, data monopolization, and trust dependency on intermediaries. To address these challenges, this paper presents the design and implementation of a decentralized marketplace built on blockchain technology. The proposed system enables peer-to-peer trading without the involvement of centralized authorities, ensuring transparency, security, and fairness among participants. Smart contracts are employed to automate transactions, enforce business rules, and eliminate the need for trusted third parties. Distributed ledger technology ensures immutability of records, while cryptographic mechanisms provide secure identity management and transaction validation. The marketplace supports secure listings, decentralized payments, dispute resistance, and trustless execution, thereby reducing operational costs and increasing user autonomy. Experimental analysis demonstrates improved reliability, resistance to tampering, and enhanced trust compared to traditional centralized platforms. The proposed decentralized marketplace framework highlights the potential of blockchain-based systems in redefining digital commerce by promoting transparency, decentralization, and user empowerment. Keywords: Decentralized Marketplace, Blockchain Technology, Smart Contracts, Peer-to-Peer Trading, Distributed Ledger, Trustless Transactions, Cryptographic Security, Transparency, Digital Commerce, Disintermediation.
Blockchain ecosystems, particularly decentralized finance (DeFi) platforms, have become high-value targets for adversaries exploiting vulnerabilities in smart contracts. Traditional honeypots designed to attract such attackers are often static and easily detectable once adversaries recognize repeating patterns. In this paper, we propose Adaptive AI-Powered Smart Contract Honeypots (AASCH), a novel deception-based security mechanism that dynamically evolves its behavior using reinforcement learning techniques. The honeypot continuously adapts to attacker strategies by modifying contract responses, transaction logic, and resource interactions in real time, thereby creating an unpredictable deception environment. We implement AASCH on the Ethereum test network and simulate various exploit scenarios, including reentrancy attacks, flash-loan exploits, and oracle manipulations. Experimental results demonstrate that AASCH significantly improves attacker capture rates (up to 37% higher than static honeypots) while reducing evasion attempts and false positives. Our findings indicate that adaptive deception is a promising defense strategy for strengthening blockchain ecosystems against evolving cyber threats.
Blockchain technology offers decentralized and secure transaction processing but suffers from critical limitations in scalability, energy efficiency, and latency, hindering its adoption in real-time high-throughput applications. This study proposes a novel Adaptive Global BestâWorst Particle Swarm Optimization (AGBWPSO) algorithm integrated with dynamic sharding to address these challenges effectively. Unlike traditional GBWPSO, the proposed AGBWPSO employs a dual-extremum influence mechanism that combines both global best and worst positions, along with adaptive nonlinear parameter adjustment strategies for the inertia weight, cognitive, and social coefficients. This enhances explorationâexploitation balance, prevents premature convergence, and ensures efficient shard reallocation under dynamic transaction loads. The integration with dynamic sharding enables parallel transaction processing across optimally configured shards, significantly improving blockchain performance metrics. Extensive simulations conducted on Ethereum, Bitcoin, Hyperledger Fabric, financial, and IoT transaction datasets demonstrate that the proposed AGBWPSO achieves up to 5.88% improvement in transaction throughput (TPS), 14.3% reduction in latency, and 20% reduction in energy consumption per transaction compared to existing optimization methods. These results establish AGBWPSO as a robust and scalable solution for enhancing the operational efficiency and sustainability of blockchain networks in real-world applications.
Evidence management comes with requirements of a visibly secure, immutable, and scalable system to drive legal proceedings with ethicacy. Where research on fully on-chain solutions shows unrealistic and extravagant costs and performance limits, the traditional off-chain centralized storage systems exhibit an insecure environment, poor traceability, and tampering concerns. ChainSEAL is a hybrid Blockchain - IPFS-based forensic Evidence Management platform that integrates IPFS for encrypted Evidence file storage, blockchain as a distributed ledger for File hash and metadata, while off-chain storage for key management. The methodology explains the system flow, that as the evidence is submitted, the FIR is generated, the case request is created, and the evidence cycle is initiated. The cycle starts with fetching the SHA-256 of the file, then encrypting the evidence, submitting it on IPFS, fetching the Content Identifier (CID) of the file on IPFS, uploading the CID + File Hash + Metadata on-chain with a maintained verifiable Chain of Custody of the Evidence cycle. This ensures confidentiality and immutability of the system. The proposed framework is empirically evaluated for cost, storage efficiency, latency, and tamper-proofness. Its legal admissibility is established through an analysis of immutability, chain of custody integrity, and role-based access control.
The urban administration in Pakistan has transformed as a result of political and economic shifts. The urban government in Pakistan has been influenced by external financing, which is a reflection of institutional reforms, fiscal decentralization, and the priorities of global development. Over the course of the last three decades, Pakistan's urban management has transitioned from a centralized bureaucratic authority to fragmented local governance systems that are shaped by donor-driven projects and conditional cash inflows. An in-depth analysis of how multilateral development banks and bilateral aid influence urban policy, infrastructure, and service delivery is presented in this specific piece of writing. The evidence demonstrates that the use of external financing has hastened the process of urban modernization while simultaneously exacerbating governance problems such as policy incoherence, accountability deficiencies, and socio-spatial inequities. In this study, political economics research and urban planning perspectives are combined in order to investigate how external funding mechanisms influence the capacities of local governments and the transformation of urban infrastructure in Pakistan's fast-growing cities. The findings highlight the necessity of having governance structures that are adaptable and, in a position, to strike a balance between local interests and global urban finance strategy.
Bambang Leo Handoko, Arta Moro Sundjaja, Evelyn Hendriana
The rapid rise in cryptocurrency presents both opportunities and challenges for retail investors due to its volatility and technological complexity. Research on investment decisions has primarily focused on behavioural finance, often overlooking how learning and literacy shape investor actions. This study addresses this gap by examining how herding behaviour, financial literacy, and digital literacy impact cryptocurrency investment decisions. Grounded in Social Learning Theory and supported by UTAUT to operationalise digital literacy, this study examines how herding behaviour, financial literacy, and digital literacy shape cryptocurrency investment decisions. We analyse survey data from 138 Indonesian retail investors through PLS-SEM. Key findings show that financial literacy (β = 0.443, t = 5.041) and digital literacy (β = 0.495, t = 4.246) are primary determinants of investment decisions, while herding behaviour (β = 0.016, t = 0.628) does not directly influence them but does so indirectly by enhancing investor literacy. This demonstrates that social observation and learning can convert herd-driven impulses into rational choices when mediated by literacy. By extending Social Learning Theory into digital investment contexts, this study provides insights for investors and policymakers seeking to enhance financial and digital literacy.
This study systematically examines the transformative role of Artificial Intelligence (AI) in addressing the persistent challenges of blockchain technology across protocols, smart contracts, and distributed ledger management. Although blockchain offers decentralization, immutability, and transparency, its broader adoption remains constrained by scalability limitations, security vulnerabilities, inefficient consensus mechanisms, and the complexity of contract design and auditing. The findings of this review demonstrate that AI provides promising solutions to these barriers. Reinforcement learning (RL) applied to Proof-of-Stake reduced consensus latency by 30-50%, while NLP-based smart contracts lowered vulnerabilities by up to 40%, though both approaches introduced new concerns related to energy overheads and auditability. In addition, intelligent algorithms enhance ledger efficiency and data analytics, supporting more scalable and secure transaction processing. Drawing on 28 peer-reviewed studies published between 2018 and 2024, and guided by the PRISMA 2020 framework, this paper synthesizes state-of-the-art research, maps sector-specific applications in finance, healthcare, and supply chain management, and highlights unresolved gaps in ethics, reproducibility, and regulatory compliance. Notably, only 12% of the reviewed studies validated their approaches on live networks underscoring the gap between simulation-driven research and real-world deployment. The discussion culminates in the AIâBlockchain Interaction Model (AIBIM), a conceptual framework that systematizes synergies across consensus, contract, and application layers. By integrating empirical insights with critical evaluation, this work emphasizes the interdisciplinary nature of AIâblockchain research and provides actionable directions for advancing decentralized, scalable, and ethically aligned systems. This synthesis provides actionable insights for developers, regulators, and researchers in deploying AI-blockchain systems across finance, healthcare, and supply chains.
The construction industry is a major global consumer of energy and a leading source of greenhouse gas emissions, underscoring the need for transparent, data-driven, and energy-efficient supply chain strategies. This study develops an integrated mixed-integer linear programming (MILP) model for a multi-echelon, multi-product construction supply chain that explicitly incorporates differentiated building energy efficiency levels ( A +, A ++, A +++) as exogenous determinants of material requirements, production processes, and logistics flows. By embedding blockchain-enabled smart contracts, the model automates supplier governance and ensures compliance with delivery reliability, quality standards, and CO 2 performance through predefined incentives and penalties, thereby enhancing transparency and accountability. The framework jointly optimizes facility location, material and product flows, supplier selection, and reverse logistics operations under a COâ emission cap, while simultaneously capturing the implications of greenfield and brownfield project conditions. A real-scale numerical case study demonstrates the modelâs ability to evaluate the economicâenvironmental trade-offs arising from increasingly stringent sustainability requirements. The results reveal that although higher energy efficiency levels incur greater initial supply chain costs due to advanced materials and more complex logistics, they lead to substantial reductions in long-term operational energy consumption, rendering the A +++ option the most economically favorable from a lifecycle perspective. Furthermore, the integration of blockchain-enabled smart contracts partially offsets cost escalations by penalizing non-compliant suppliers and rewarding high-performing ones. Overall, the proposed model provides a rigorous and transparent decision-support framework that enables contractors to align supply chain design with energy-efficiency targets, CO 2 -reduction policies, and circular-economy objectives while preserving operational feasibility and supply reliability.
Jack McGarrigle, Jessica Smith, J. Gwyn Griffiths, Jamie Torrance ¡ 6 authors
Background and aims: Dark patterns are online platform design features that influence consumer behaviour to the advantage of the interface designer. In online gambling, such designs may exacerbate gambling-related harms, particularly among vulnerable consumers. This study aims to provide the first scoping review of dark patterns in online gambling. Methods: Following established scoping review frameworks, we systematically searched databases and grey literature using terms related to dark patterns and online gambling. The review protocol was preregistered. Results: Included articles (n = 16) addressed a variety of gambling-related dark patterns: hidden gambling management tools, inducements with complex conditions, minimum balances required to withdraw funds, unnecessary frictions involved in closing an account, high defaults in stake, deposit, reality check and deposit limit settings, and urgency-based gambling prompts. To address inconsistent terminology across studies, we synthesised existing literature by mapping identified dark patterns to a transdisciplinary framework, providing greater conceptual clarity and direction for future research. Discussions and conclusions: The potential for harm from dark patterns is evident, yet evidence on behavioural impacts is limited, hindered by restricted access to proprietary gambling operator data. Research in this area is sparse and fragmented, often using inconsistent terminology. Future studies should empirically investigate the influence of dark patterns on consumer behaviour, especially among vulnerable populations, and evaluate safer design alternatives. We recommend mandating gambling operators to collaborate with researchers to assess platform safety, and shifting the burden of proof onto operators to demonstrate that their platforms prioritise consumer safety and foster responsible gambling environments.
<p><span lang="EN-US" style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 115%; font-family: 'Times New Roman',serif; mso-fareast-font-family: ĺŽä˝; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Amidst the rise of Web3, a technology transforming user interactions and challenging corporate control, this study uses a hybrid model of appreciative inquiry that matches the remote and decentralized nature of Web3 communities, to investigate the formation of a blockchain startup and its emergent culture and values. Despite limited resources, the company has built a diverse, global community via digital platforms, exceeding stakeholder expectations. This appreciative inquiry uncovers a community manifesting five core values: excellence, sustainable innovation, inclusivity, continuous learning, and creativity, challenging stereotypes often associated with the Web3 industry. This work advances participative research by introducing a hybrid model of appreciative inquiry tailored for remote and decentralized Web3 communities. By adapting appreciative inquiry to the unique dynamics of blockchain-dependent organizations, this study extends the methodology&rsquo;s applicability and demonstrates its effectiveness in uncovering and fostering core communal values within cutting-edge technological contexts.</span></p>
We introduce Auditable Proof-of-Work (APoW), a novel proof-of-work (PoW) construction inspired by Hashcash-style nonce searching, which enables the auditing of other miners' work through accountable re-scanning of the nonce space. The proposed scheme allows a miner to probabilistically attest to having searched specified regions of the nonce space in earlier mining rounds, while concurrently earning rewards for performing productive work for a new block or pool share. This capability enables miners belonging to a mining pools to audit another miner's claimed effort retroactively, thereby allowing the probabilistic detection of block withholding attacks (BWAs) without requiring trusted hardware or trusted third parties. As a consequence, the construction supports the design of decentralized mining pools in which work attribution is verifiable and withholding incentives are substantially reduced. The scheme preserves the fundamental properties of conventional PoW, including public verifiability and difficulty adjustment, while adding an orthogonal auditability layer tailored to pool-based mining. Finally, while a full deployment of APoW in Bitcoin would require a consensus rule change and minor modifications to mining ASICs, the construction remains practically useful even without consensus changes, for instance, as a pool-level auditing mechanism that enables verifiable pay-for-auditing using existing pool reserves.
Francisco Angulo de Lafuente, Vladimir Veselov, Richard Goodman
We propose a theoretical framework--Holographic Reservoir Computing (HRC)--which hypothesizes that the thermodynamic noise and timing dynamics in voltage-stressed Bitcoin mining ASICs (BM1366) could potentially serve as a physical reservoir computing substrate. We present the CHIMERA (Conscious Hybrid Intelligence via Miner-Embedded Resonance Architecture) system architecture, which treats the SHA-256 hashing pipeline not as an entropy source, but as a deterministic diffusion operator whose timing characteristics under controlled voltage and frequency conditions may exhibit computationally useful dynamics. We report preliminary observations of non-Poissonian variability in inter-arrival time statistics during edge-of-stability operation, which we term the "Silicon Heartbeat" hypothesis. Theoretical analysis based on Hierarchical Number System (HNS) representations suggests that such architectures could achieve O(log n) energy scaling compared to traditional von Neumann O(2^n) dependencies. However, we emphasize that these are theoretical projections requiring experimental validation. We present the implemented measurement infrastructure, acknowledge current limitations, and outline the experimental program necessary to confirm or refute these hypotheses. This work contributes to the emerging field of thermodynamic computing by proposing a novel approach to repurposing obsolete cryptographic hardware for neuromorphic applications.
Prediction markets are designed to aggregate dispersed information about future events, yet today's ecosystem is fragmented across heterogeneous operator-run platforms and blockchain-based protocols that independently list economically identical events. In the absence of a shared notion of event identity, liquidity fails to pool across venues, arbitrage becomes capital-intensive or unenforceable, and prices systematically violate the Law of One Price. As a result, market prices reflect platform-local beliefs rather than a single, globally aggregated probability, undermining the core information-aggregation function of prediction markets. We address this gap by introducing a semantic alignment framework that makes cross-platform event identity explicit through joint analysis of natural-language descriptions, resolution semantics, and temporal scope. Applying this framework, we construct the first human-validated, cross-platform dataset of aligned prediction markets, covering over 100 000 events across ten major venues from 2018 to 2025. Using this dataset, we show that roughly 6% of all events are concurrently listed across platforms and that semantically equivalent markets exhibit persistent execution-aware price deviations of 2-4% on average, even in highly liquid and information-rich settings. These mispricings give rise to persistent cross-platform arbitrage opportunities driven by structural frictions rather than informational disagreement. Overall, our results demonstrate that semantic non-fungibility is a fundamental barrier to price convergence, and that resolving event identity is a prerequisite for prediction markets to aggregate information at a global scale.
Abstract Green entrepreneurship has emerged as a key driver of sustainable market transformation, linking innovation, environmental stewardship, and social equity. Green entrepreneurship plays a vital role in enabling low-carbon growth by introducing innovative solutions that mitigate environmental impact while generating socio-economic value. The emergence of carbon markets provides a new economic mechanism to reward emission reduction activities, thereby creating sustainable market opportunities. This research explores how carbon finance mechanisms, including carbon credits, offset projects, and nature-based solutions, can support the growth of green enterprises. The study examines the potential of decentralized community-led green entrepreneurship models like coir and bamboo to participate in carbon markets and contribute to sustainable market ecosystems using digital tools and online platforms. It aims to explore how rural fibre-based industries can leverage carbon finance mechanisms to achieve environmental sustainability while enhancing rural livelihoods. By investigating the research intersection of low-carbon innovation, community enterprise, and carbon monetization, this research positions Online Green Entrepreneurship as a transformative pathway for building in Sustainable Markets Ecosystem. Keywords: Green Entrepreneurship, Sustainable Markets, Digital Tools, Online Platforms, Low-Carbon Innovation, Carbon Markets, Carbon Finance, Rural Livelihoods, Community Enterprise, Socio-Economic Value, Ecosystem.
Oana Panazan, Catalin GHEORGHE, Aamir Aijaz Syed, Ahmed Jeribi
This study examines the dynamic interactions between precious metals, cryptocurrencies, stablecoins, safe-haven currencies, and two key macroeconomic indicators, the 5-year breakeven inflation expectation (T5YIE) and the 10-year minus 3-month Treasury yield spread (T10Y3M), over January 2016âJuly 2025. To capture nonlinear and multi-scale dependencies, the study applies Quantile-on-Quantile Regression (QQR) in combination with wavelet coherence (WCO) and wavelet transform coherence (WTC). The results indicate that major cryptocurrencies such as Bitcoin and Ethereum do not display robust or systematic links with inflation expectations or recession risk, limiting their role as macro-financial hedges. By contrast, the Japanese yen and Swiss franc show pronounced tail sensitivities, reaffirming their safe-haven status, while gold and its tokenized counterparts (DGX, PAXG) exhibit persistent long-run coherence with inflation expectations. Stablecoins demonstrate unstable short-term linkages shaped by liquidity shocks and market frictions. The research provides new evidence on the heterogeneous roles of digital and traditional assets in shaping macroeconomic expectations. The findings carry implications for investors, who should continue to rely on gold and safe-haven currencies for crisis hedging, and for regulators concerned with the systemic stability of emerging digital instruments.
Lina Bautista LĂłpez, Edgar Esaul Vite GĂłmez, Lizet Manzo MartĂnez
This article offers a multidisciplinary approach to the study of cryptocurrencies through the analysis of different academic documents. Analysis is an effort to address the issue of such digital assets from an overview rather than a particular one. The objective is that cryptocurrencies are understood in their concept, origin and operation by those interested in the subject who are not immersed in it. Therefore, two theories that are the monetary theory and the economic theory of the law are considered to support the research in its several aspects such as the economic, legal, social, among others. The analysis makes it possible to identify common trends in the authors without departing from their own opinion of cryptocurrencies considering their discipline.
For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex scenarios where traditional methods struggled. As data availability continues to expand, the integration of machine learning techniques is likely to further enhance forecasting capabilities across various fields. Today, hybrid techniques are gaining popularity, as they combine the advantages of different approaches to deliver improved predictive performance and more advanced visualization analytics for decision support. These hybrid approaches can provide better prediction, and at the same time, they can develop a more sophisticated set of visualization analytics for decision support. Recently, the integration of cross-entropy, fuzzy logic, and attention mechanisms in hybrid forecasting models has enhanced their ability to capture complex and uncertain patterns in financial and energy markets. In this study, we propose a hybrid ANNâLSTM deep learning model optimized with cross-entropy, fuzzy logic, and an attention mechanism to enhance the forecasting of financial and energy time series, specifically Ethereum and natural gas prices. Our models combine the feature extraction strength of ANN with the temporal learning of LSTM, while cross-entropy improves convergence, fuzzy logic handles uncertainty, and attention refines feature weighting. Since inaccurate forecasts can lead to greater estimation uncertainty and increased financial and operational risk, improving predictive reliability is essential for effective risk mitigation. These techniques prove effective not only in improving estimation accuracy but also in minimizing financial risks and supporting more informed investment decisions.
The convergence of the Internet of Things and edge computing represents a fundamental transformation in distributed computing architecture. Traditional cloud-centric models introduce latency and connectivity dependencies flawed for time-touchy packages. Side computing addresses such constraints by positioning computational sources at network peripheries. Distributed processing paradigms restructure data pipelines through intermediate layers between endpoint devices and centralized infrastructure. Fog nodes extend cloud capabilities to locations where data originates. Tiered computation models distinguish between device-level processing, gateway computation, and cloud-based analytics. Aspect synthetic intelligence allows deployment of state-of-the-art machine learning models on resource-limited hardware. Neural network compression strategies consisting of quantization and pruning lessen version complexity while keeping accuracy. Fifth-generation wireless networks provide a connectivity fabric essential for distributed deployments. Multi-access edge computing positions processing resources at radio access network edges. Computation offloading transfers tasks from mobile devices to edge servers strategically. Security frameworks address expanded attack surfaces through zero-trust models and blockchain-based identity management. Distributed ledger architectures eliminate centralized credential repositories. Smart contracts automate security policy enforcement across edge networks reliably.
Purpose The absence of cryptocurrency (CC) tax regulations in many countries raises concerns about compliance and potential revenue losses. Understanding the factors that drive CC holdersâ tax propensity is crucial for developing effective tax policies. Therefore, this research aims to explore the influence of contextual factors, e.g. CC legitimacy, CC investment risks, and social responsibility, and individual factors, e.g. attitude towards CC tax payment, technological competence and CC financial literacy, on CC tax payment propensity. Additionally, the study delves into the moderating role of financial literacy in the proposed model. Design/methodology/approach An integrated model of TPB-STC (theory of planned behaviour and social cognitive theory) was grounded in this study. Data were collected using a cross-sectional research approach through an online survey responded by CC investors. Findings The study found that attitude towards CC tax payment, social responsibility, legitimacy and CC financial literacy exerted a positive effect on the propensity to pay tax on CC. However, CC investment risks demonstrated a negative effect on propensity. Interestingly, the CC financial literacy-moderated interactions of crypto assets' legitimacy, technological competence and investment risks on CC tax payment propensity were significant. Practical implications The discoveries that emerged from this study contain practical and actionable insights for stakeholders, including regulators, tax authorities and investors. Educational programs focused on enhancing CC financial literacy should be integrated into public finance initiatives to improve taxpayersâ understanding of crypto taxation. Additionally, regulatory bodies can collaborate with crypto exchanges to implement transparent reporting mechanisms, making tax compliance more accessible and straightforward for investors. These actionable steps can help foster a proactive tax-paying culture, even in the absence of formal tax regulations. Originality/value This study provides a theoretical foundation of tax practices behaviour related to crypto in decentralized financial markets, paving the way for future research on self-regulating mechanisms within the present-day fast-moving crypto market.