Blockchain Papers

Alexandre Rigaud

No abstract is available for this record.

Jainish Bhagat

Supply chain finance (SCF) plays a pivotal role in maintaining liquidity and operational continuity across global value networks. However, systemic supply chain disruptions, macroeconomic volatility, and information asymmetry frequently expose SCF programs to severe friction and default risks. While digital transformation is widely touted as a catalyst for supply chain resilience, empirical evidence regarding the explicit mechanisms through which distinct digital transformation capabilities enhance Supply Chain Finance Resilience (SCFR) remains fragmented. Grounded in the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and Information Processing Theory (IPT), this study develops and tests an integrated framework evaluating the direct and indirect impacts of Artificial Intelligence Capability (AIC), Blockchain Capability (BC), and Data Analytics Capability (DAC) on SCFR, mediated by Digital Trust in SCF Platforms (DT).Using a computational research simulation methodology, a respondent-level dataset (N=500) representing supply chain, finance, operations, and IT decision-makers across international enterprises was algorithmically generated under a defensible latent-variable covariance structure. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000 bootstrap resamples was executed to evaluate the measurement and structural models. The structural analysis reveals that AIC (β=0.241,p<.001), BC (β=0.312,p<.001), and DAC (β=0.284,p<.001) significantly and positively drive Digital Trust in SCF Platforms, explaining 54.2% of its variance (R^2=0.542). Digital Trust, in turn, exerts a substantial direct effect on SCFR (β=0.385,p<.001). Furthermore, direct effects on SCFR were confirmed for DAC (β=0.218,p<.001) and AIC (β=0.152,p=.002), whereas the direct link from BC to SCFR was non-significant (β=0.071,p=.158). Formal mediation testing using percentile bootstrapping confirmed that Digital Trust fully mediates the relationship between Blockchain Capability and SCFR, while partially mediating the relationships for AIC and DAC. The overall structural model accounts for 58.6% of the variance in Supply Chain Finance Resilience (R^2=0.586,Q_"predict" ^2=0.412).This methodological prototype advances theoretical understanding by unpacking the granular capability configurations necessary to foster digital trust and financial resilience in supply networks. For practitioners and policymakers, the findings highlight that investing in blockchain technology yields minimal resilience benefits unless coupled with platform-wide digital trust mechanisms, whereas AI and analytics offer dual-pathway benefits across operational and relational domains.

José Luis Alberto Delgado, Dilek Demirbaş

This study investigates whether cryptocurrency adoption has affected Argentina’s bilateral trade flows within a gravity-model framework. While blockchain-based technologies are often expected to reduce transaction costs and facilitate international trade, quantitative evidence on their actual impact remains limited. Using panel data on Argentina’s trade with its main partners, the analysis combines standard gravity variables with country-level measures of cryptocurrency activity and estimates fixed effects, random effects, and high-dimensional fixed effects models.The results confirm the continued relevance of traditional trade determinants. Distance shows a robust negative effect on bilateral trade, with an elasticity ranging from −0.54 to −1.65 (p<0.05) across specifications. Country contiguity is associated with a 3.5-fold increase in bilateral trade (coefficient: +1.25, p<0.01). The effect of cryptocurrency adoption, by contrast, varies across specifications: in the random effects model, it is negatively associated with formal trade (−0.049, p<0.01), while in the correctly specified PPML model with origin-destination-year fixed effects, the contemporaneous effect is statistically insignificant. However, when cryptocurrency adoption is lagged one period, it shows a positive and highly significant association with trade (+0.061, p<0.01), suggesting that the trade-facilitating effect of crypto infrastructure may operate with a delay. We also find marginal evidence (p≈0.10) that cryptocurrency adoption attenuates the trade-reducing effect of distance. This counterintuitive result may indicate that cryptocurrency adoption substitutes for formal trade channels or reflects periods of economic instability, including the COVID-19 pandemic. However, this relationship is not robust to more demanding specifications that control for unobserved heterogeneity.Overall, the findings suggest that blockchain-based technologies have not yet translated into measurable trade-facilitating effects, partly due to limited institutional support and legal uncertainty. The paper highlights the gap between the potential benefits of blockchain for international trade and its actual adoption, emphasising the role of coordinated institutional frameworks in enabling technological diffusion.

Houda BenMabrouk, Safa Boukadida, Khaled Guesmi

Purpose The study investigates the effect of investor fear on cryptocurrency crash risk, with emphasis on overall market sentiment and COVID-19-related fear. It also evaluates the relative performance of Google search-based measures compared to the economic policy uncertainty (EPU) index and the volatility indexes (VIX) as benchmark indicators of uncertainty. Design/methodology/approach This study employs a quantitative empirical approach to examine the impact of investor fear on cryptocurrency price crash risk. Investor sentiment is proxied using the FEARS index derived from Google search volumes and the coronavirus fear index. Crash risk is measured using negative conditional skewness of weekly returns and down-to-up volatility. The analysis is based on weekly data for the top 10 cryptocurrencies from August 2010 to October 2021. Regression models are used to examine the relationship between investor fear and crash risk and to compare the explanatory power of Google-based fear indicators with traditional uncertainty measures. Findings The results show that investor fear significantly increases the risk, while COVID-19-related fear further intensifies this effect, highlighting the vulnerability of crypto markets during periods of heightened uncertainty. Moreover, Google-based fear indicators outperform the EPU index and the VIX in explaining and predicting crash risk. Overall, the findings suggest that investor attention and sentiment are more powerful drivers of cryptocurrency crash risk than traditional volatility-based measures. Originality/value This study links investor fear, including COVID-19 sentiment, to cryptocurrency crash risk and finds that Google-based fear indicators outperform traditional measures like the EPU index and the VIX in predicting market downturns.

Tomiwa Sunday Adebayo, Berna Uzun

This study investigates how economic policy uncertainty (EPU) innovations shape the daily returns of major cryptocurrencies, namely ADA, USDT, ETH, USDC, BTC, BCH, XRP, BNB, DOGE, and LTC. Using daily data from 07/06/2020 to 01/01/2026, the study applies symmetric and asymmetric wavelet quantile regression to capture state dependence across the conditional return distribution and horizon dependence across short-, medium-, and long-run components. The symmetric results reveal that the EPU—cryptocurrency nexus is heterogeneous, time-varying, and strongly dependent on both investment horizon and return quantile. In the short term, EPU generally has weak or insignificant effects across most cryptocurrencies. However, the medium-term results show stronger and more diverse responses, with ADA, LTC, DOGE, USDC, and BNB displaying positive effects at extreme lower and higher quantiles, while negative effects are mostly concentrated around middle quantiles. Conversely, BCH, USDT, ETH, and BTC exhibit stronger negative medium-term responses across most quantiles. In the long term, EPU mainly exerts adverse effects on ADA, LTC, DOGE, USDC, BNB, ETH, and BTC. The asymmetric findings further confirm that positive and negative EPU shocks transmit differently into cryptocurrency returns. Positive EPU shocks often generate negative medium- or long-term effects, whereas negative shocks frequently produce positive medium-term responses, particularly for LTC, DOGE, BCH, BNB, XRP, and USDT. Based on these findings, policy recommendations are proposed.

Andrea Caravaggio, Silvia Leoni

Abstract The management of infectious diseases increasingly relies on innovative but costly pharmaceutical treatments, raising complex trade-offs between epidemiological containment, fiscal sustainability, and institutional coordination. We develop a spatially structured agent-based model in which decentralized health authorities allocate treatment under local budget constraints while infection spreads across a two-dimensional lattice through neighborhood spillovers. Within each location, treatment intensity is chosen endogenously, interacting with local GDP dynamics and pricing conditions. Simulation results reveal that purely decentralized optimization mitigates but does not reverse infection growth within policy-relevant horizons, generating persistent spatial heterogeneity in both epidemiological and economic outcomes. We then introduce bounded spatial policy interaction, showing that partial coordination substantially improves containment but may increase the persistence of fiscal engagement. Extending the model to heterogeneous and time-varying pricing, we find that price discrimination amplifies medium-run infection and fiscal pressure under decentralization. However, when surplus revenues finance endogenous R&amp;D, treatment efficacy improves over time, generating a feedback mechanism in which innovation mitigates long-run epidemiological and economic losses. Our findings highlight the critical interplay between spatial structure, decentralized decision-making, pricing design, and innovation incentives in shaping epidemic outcomes. Effective management of high-cost treatments requires not only medical efficacy but also institutional coordination and carefully designed market mechanisms.

H T Wang, Ruonan Zhang, Xuebing Bai

ABSTRACT Financial innovation profoundly reshapes the financing mechanisms, risk structures, and resilience of agricultural value chains. Based on 40 high‐quality English studies (2018–2026), this review identifies five core agricultural financial innovations: digital payments, digital credit, supply chain finance, blockchain, and decentralized finance, as well as climate derivatives and blended finance. These innovations enhance financial inclusion for smallholders and agribusinesses by mitigating information asymmetry and easing financing constraints. The integration of green finance and digital inclusive finance lifts agricultural green total factor productivity and promotes eco‐friendly technology adoption. The synergy of supply chain finance, AI, and blockchain strengthens the shock resistance, recovery, and adaptive transformation capabilities of agricultural value chains. Constraints include the digital divide, technological uncertainty, insufficient regulation, and unsustainable business models. This paper constructs a “technology–institution–value” framework to illustrate transmission pathways and puts forward future research directions and policy implications.

Remy Jonkam Oben, Aliya Zhakanova Isiksal

The amount of international capital invested in sustainability-focused investments and decentralized financial technologies has been growing fast. Thus, this research focuses on the transmission of volatility and optimal portfolio composition among decentralized finance (DeFi) assets, S&P renewable energy and technology market indices, and conventional energy commodities for the period from March 15, 2018, to August 30, 2024. The sample period was divided into three sub-periods to examine the impact of COVID-19, which increased in parallel with the adoption of DeFi and a focus on sustainability: pre-COVID, during-COVID, and post-COVID. This research utilizes the Diebold-Yilmaz and Baruník-Křehlík techniques for time-and frequency-domain analyses, and the Dynamic Conditional Correlation model for portfolio optimization. First, the findings reveal that DeFi tokens (sustainable markets) (brown investments) display moderate (high) (very low) internal connectedness. Second, DeFi tokens demonstrate very low volatility connectedness with both sustainable and brown markets, which suggests strong diversification effects. Third, volatility connectedness among sustainable markets and conventional energy commodities is equally low. Fourth, sustainable markets (conventional energy commodities) make the highest (lowest) contribution to total volatility connectedness, and they operate as net transmitters (receivers) of volatility. Moreover, the total volatility connectedness is 33.7%, which is relatively low, suggesting significant opportunities for diversification of investment portfolios. Furthermore, the outcomes for optimal portfolio weights present greater allocations to green markets compared to conventional energy commodities and DeFi assets, revealing an escalating global transition toward sustainability. Additionally, COVID-19 significantly influenced volatility transmissions and portfolio allocations.

Yaser Sadati-Keneti, Mohammad Vahid Sebt, Reza R. Tavakkoli-Moghaddam, Orod Ahmadi

The aim of this research is to employ improved machine learning techniques to determine the best Bitcoin trading positions in response to sudden price changes caused by global emergencies such as pandemics, conflicts, and economic disputes. Specifically, this study examines price fluctuations during the COVID pandemic as a case study to evaluate the performance of the algorithms investigated. We present a novel hybrid approach that merges Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Decision Tree (DT) classification to effectively eliminate noisy data and extract pertinent information for accurate position forecasting. The DBSCAN algorithm organizes the data to reveal important patterns, while the DT classifier sorts the trading signals. The performance of the proposed DBSCAN-DT model is rigorously compared with established alternatives, including the Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), and traditional Decision Trees. Findings from the experiments show that the DBSCAN-DT hybrid consistently outperforms these benchmarks during the outbreak, epidemic, and pandemic phases of COVID, attaining greater accuracy in forecasting both trading positions and market trends. These findings emphasize the essential importance of incorporating pandemic-related disruptions into cryptocurrency price prediction models and showcase the flexibility of our method in addressing sudden market changes.

Nasir Khan, Khaled Guesmi, Tong Su, Brian Lucey

ABSTRACT The study examines interconnectedness among categories of cryptocurrencies, healthcare cryptocurrencies, decentralized finance indices (DeFi), and non‐fungible tokens using TVP‐VAR extended joint connectedness, based on daily data from September 4, 2019, to July 31, 2024. The outcomes indicate that DeFi is the return shock and leading net transmitter, while healthcare cryptocurrencies are the net receivers. Secondly, we also analyze the effect of four news‐based global uncertainties on total returns using the BVAR model. The outcomes reveal that geopolitical risk (GPR) does not significantly influence global connectedness; however, some individual DeFi protocols, such as Chain Link, Tezos, and Maker, respond positively to GPR. Conversely, economic policy uncertainty reduces the total connectedness index, while infectious disease equity market volatility increases it. Using weekly data covering cryptocurrency uncertainty indices, exerts a positive effect on total return connectedness. The findings underline the influential crypto assets and should be monitored by investors and policymakers.

Pengcheng Wang, Yanyan Shang, Zefeng Bai

Purpose Do individuals take more financial risks when faced with a health crisis? This study examines the impact of COVID-19 on individuals' propensity to invest in cryptocurrencies. Design/methodology/approach We applied a probit model to the restricted version of 2021 data from the National Financial Capability Study (NFCS). We then combined propensity score matching (PSM) with an instrumental variable (IV) approach to address potential endogeneity concerns. Findings We found that individuals experiencing a health crisis, proxied by COVID-19 infection, demonstrate a significant tendency to take financial risks, proxied by investment in cryptocurrencies. Furthermore, the established link between exposure to a health risk and investing in high-risk financial products is more pronounced among individuals without financial education. Originality/value To the best of our knowledge, this investigation is the first to show how consumer health status affects the propensity to invest in cryptocurrency. We provide timely insights into how external mortality reminders drive risky financial decisions. Our main finding runs contrary to the traditional economic literature, which suggests that people maintain a certain level of risk tolerance and therefore adjust their financial investment strategies to mitigate, not exacerbate, increased risk.

Ainur Rohma, Ris Yuwono Yudo Nugroho

This study explores the key determinants influencing cryptocurrency in Indonesia, focusing on macroeconomic variables including inflation, money supply, gold prices, and crude oil prices over the period from 2013 to 2023. It investigates the dynamic relationships between these variables and Bitcoin, the most widely recognized cryptocurrency globally. The research offers a novel contribution by integrating both domestic economic indicators and external commodity prices into a comprehensive framework for cryptocurrency pricing tailored specifically to the Indonesian market context. This innovative and comprehensive approach significantly enhances the understanding of how macroeconomic factors interact with cryptocurrency behavior, which is crucial for various stakeholders and policymakers alike. The findings aim to provide valuable insights to support the formulation of effective monetary policies in an evolving, increasingly complex financial landscape. Future studies are encouraged to build upon this framework by examining the connections between cryptocurrency and other components of the broader financial system.

Tuna Can Güleç, Elif Erer, Selim Duramaz

Abstract This study explores the higher-order moments of connectedness among cryptocurrency, commodity, bond, and stock markets from April 19, 2017, to December 29, 2023, on the basis of the GARCH-SK and TVP-VAR models. The findings reveal that Bitcoin and Ethereum act as significant net shock transmitters, especially during major events such as the COVID-19 pandemic and the Russia–Ukraine conflict. After mid-2021, these cryptocurrencies transitioned from net receivers to net transmitters of volatility owing to rising economic and geopolitical risks. These insights assist in portfolio diversification strategies. By combining shock transmitters with shock-resilient cryptocurrencies, investors can enhance their risk profiles. Diversification opportunities shift during financial crises, making it crucial to focus on shock transmitters, which are less influenced by various risk factors. Additionally, the study highlights cryptocurrencies as potential safe havens compared with traditional assets such as gold, bonds, and stocks, which often maintain or appreciate value during market stress. TVP-VAR-informed dynamic portfolio reallocation can improve risk-adjusted returns and lower volatility, aiding in capital preservation during high TCI periods. Overall, our findings suggest that portfolios that include cryptocurrencies generally outperform those that do not, emphasizing their role as effective diversifiers in portfolio optimization and financial stability.

Janesh Sami

No abstract is available for this record.

Patrick Kiefer, Michael Nowotny

We document significant reversal in cryptocurrency returns at 8-and 10-week horizons, concentrated in midsize, relatively volatile assets. Using a panel of 70 USDT-quoted tokens on Binance from January 2021 through March 2026, we show that a contrarian strategy of buying past losers and selling past winners, by forming Jegadeesh-Titman (1993) calendar-time overlapping portfolios, earns a 39.6% annualized return (Sharpe 0.96, Newey-West t = 2.10). Reversal is stronger among high-volatility assets, generating a Sharpe ratio of 1.37 (t = 3.19), and is strengthened outside of the largest assets, generating a Sharpe ratio of 1.69 (t = 3.80). The effect is robust across tercile, quintile, and decile sorts; skip period variants; inverse-volatility weighting; and temporal subsamples. A circular block bootstrap with 10,000 replications corroborates the high-volatility result nonparametrically with 95% of Sharpe ratios above 0.67, and the high-versus-low volatility gap positive in 94% of replications. Several economic mechanisms to rationalize these findings are discussed, including the tendency of treasury managers to sell into upswings, and Nagel's (2012) theory that reversal compensates liquidity providers.

Yizhou Wen, Kani Chen

In early November 2025 the yield-bearing stablecoin sector experienced its first systemic run: over roughly seventy-two hours, three synthetic dollar tokens lost between 94 and 99 percent of their value, set off by the disclosure of an external-manager loss at Stream Finance. Using hourly on-chain data we reconstruct the cascade and show that survival was not determined by on-chain exposure or scale-the largest instrument, sUSDe, held its peg while absorbing several hundred million dollars of redemptions-but by the quality of the backing and whether redemptions were honored under stress. We further show that the contagion did not travel through observable decentralized-finance composability: public lending exposure to the failed collateral was negligible. Transmission ran instead through off-chain reserve relationships and, in the single material public exposure, through a price oracle that remained frozen at the pre-crash value, implying a roughly 119-fold overvaluation weeks into the collapse, so that no liquidation fired and approximately $7.5 million of bad debt accrued without a single onchain bad-debt event. We read the episode as evidence that opacity in valuation, rather than composability, was the systemic channel, and draw implications for the disclosure, redemption, and oracle requirements that govern tokenized dollars. The paper is a descriptive and structural anatomy of one systemic episode; we make no causal-identification claim.

Hayfa Kazouz, Mohamed Yousfi

No abstract is available for this record.

M. S. F. Nasrifa, R. P. D. M. Amarasinghe, W. M. P. K. Weerasinghe

The purpose of this research is to explore how investor attention, measured by GSVI, influences cryptocurrency market behavior under varying conditions. For this the study examines the impact of Google Search Volume Index (GSVI) on cryptocurrency returns, considering market uncertainty, news sentiment, and the COVID-19 pandemic. A regression analysis was conducted using datasets covering BNB, Bitcoin, Dogecoin, Solana, and Tether from 2015 to 2022. Stata was used to estimate the relationships between cryptocurrency returns and key variables, ensuring accurate and reliable results to quantify the relationships. Our findings indicate that abnormal increases in GSVI positively affect cryptocurrency returns, particularly during high uncertainty periods and when news sentiment is favorable. Moreover, the effect of investor attention on returns was significantly amplified during the COVID-19 pandemic, suggesting that global crises has heightened the role of behavioral factors in cryptocurrency markets. This research contributes to the literature by integrating investor attention with uncertainty and sentiment measures, offering a comprehensive view of cryptocurrency price dynamics. Unlike previous studies that examine these factors in isolation, our study highlights their combined effect, providing valuable insights for investors, policymakers, and analysts in understanding market trends and decision-making strategies.

Amro Saleem Alamaren, Abdelhak Lefilef, Thair Kaddumi, Sami Bendjeddou · 5 authors

The study examined the connectedness among bitcoin, green bonds (represented by the US S&amp;amp;P Green Bond Index), renewable energy (represented by the OMX Biofuel Index), and gold, utilizing a novel quantile connectedness approach from 14 November 2017 to 30 May 2024. This approach contributes to understanding the transmission mechanisms, influence, and connectedness among the bitcoin, green bond, renewable energy, and gold markets. The result indicates that significant values appear at specific intervals. A significant spike was observed at specific intervals around 2019, mainly due to the trade war between the U.S. and China. A subsequent shock occurred between 2020 and 2021, driven by the COVID-19 pandemic. Moreover, the US credit crisis exacerbated volatility spillovers and financial contagion across markets, worsening these effects in 2023 and intensifying volatility spillovers and financial contagion across markets, exacerbating their outcomes. Additionally, the results suggest that Bitcoin primarily serves as a receiver of shocks. At the same time, the green bond transmits the shocks, and renewable energy and gold have switched between transmission and receiving shock roles during the period. The findings offer valuable insights into sustainable portfolio construction, highlighting that green bonds serve as primary transmitters of shocks and suggest a role as diversification anchors during market stress. Additionally, recognizing Bitcoin as a shock absorber and the shifting roles of renewable energy and gold help investors optimize risk-hedging strategies and enhance portfolio resilience across varying market conditions. This indicates that understanding how these assets correlate across various market scenarios is crucial to maximizing portfolio performance while accounting for sustainability constraints.

SANJAY V S, R.V Suganya

Abstract The digital transformation of global supply chains presents unprecedented opportunities, yet it concurrently exacerbates the existing gap in financial inclusion for Micro, Small, and Medium Enterprises (MSMEs). Traditional supply chain finance (SCF) models often fail to serve these small suppliers due to high information asymmetry, lack of verifiable collateral, and manual, paper-intensive processes, leading to significant liquidity constraints. This study proposes and empirically investigates blockchain technology as a foundational solution to mitigate these challenges. Specifically, it examines how blockchain-enabled traceability fosters greater trust and transparency, which in turn facilitates more accessible and inclusive supplier financing mechanisms. Employing a mixed-method approach (Quantitative N=150−180 survey and Qualitative interviews) with a cross-sectional design, the research analyzes relationships using descriptive statistics, regression, and factor analysis. Preliminary findings are expected to demonstrate a significant positive impact of blockchain adoption on financial inclusion metrics for MSMEs. The research contributes by providing a rigorous framework for practitioners and policymakers aiming to leverage decentralized technology to create a more equitable and sustainable global trade ecosystem. Keywords: financial inclusion, blockchain enabled supply, micro, small, and medium enterprises,

Yuexin Xiang, Qishuang Fu, Yuquan Li, Qin Wang · 6 authors

Memecoins, emerging from internet culture and community-driven narratives, have rapidly evolved into a unique class of crypto assets. Unlike technology-driven cryptocurrencies, their market dynamics are primarily shaped by viral social media diffusion, celebrity influence, and speculative capital inflows. To capture the distinctive vulnerabilities of these ecosystems, we present the first Memecoin Ecosystem Fragility Framework (ME2F). ME2F formalizes memecoin risks in three dimensions: i) Volatility Dynamics Score capturing persistent and extreme price swings together with spillover from base chains; ii) Whale Dominance Score quantifying ownership concentration among top holders; and iii) Sentiment Amplification Score measuring the impact of attention-driven shocks on market stability. We apply ME2F to representative tokens (over 65% market share) and show that fragility is not evenly distributed across the ecosystem. Politically themed tokens such as TRUMP, MELANIA, and LIBRA concentrate the highest risks, combining volatility, ownership concentration, and sensitivity to sentiment shocks. Established memecoins such as DOGE, SHIB, and PEPE fall into an intermediate range. Benchmark tokens ETH and SOL remain consistently resilient due to deeper liquidity and institutional participation. Our findings provide the first ecosystem-level evidence of memecoin fragility and highlight governance implications for enhancing market resilience in the Web3 era.

Maria Vlachou, Konstantinos Ν. Konstantakis, Kyriakos Drivas, Panayotis G. Michaelides

No abstract is available for this record.

Tanvi Gulati, Anju Singla, Poonam Saini

Purpose This review systematically examines the convergence of Sustainable Digital Finance and Finance 5.0, highlighting their role in advancing financial sustainability, inclusion, and technological innovation. Finance 5.0 represents a transition from profit-driven finance to a human-centric, ethical, and sustainability-aligned financial ecosystem, where Artificial Intelligence (AI), blockchain, Decentralized Finance (DeFi), quantum computing, and RegTech enhance transparency, Environmental, Social, and Governance (ESG) compliance, and financial accessibility. Design/methodology/approach A Systematic Literature Review (SLR) was conducted using the ADO-TCM framework, which organizes research findings into antecedents, decisions, outcomes, theories, contexts, and methodologies. A structured search strategy was conducted across peer-reviewed literature using Scopus and Web of Science databases (2015–2025). Findings The findings indicate the role of Finance 5.0 in advancing sustainable financial ecosystems through AI-driven ESG analytics, blockchain-powered impact investing, and Digital currency-enabled financial inclusion. However, regulatory fragmentation, ethical AI concerns, and financial accessibility disparities remain significant challenges. The findings emphasize the need for standardized ESG metrics, ethical AI governance, and scalable financial policies to bridge sustainability gaps. Additionally, emerging technologies such as quantum computing, DeFi-driven climate finance, and AI ethics in financial decision-making require further exploration to enhance transparency, efficiency, and sustainability in digital financial ecosystems. Originality/value This review presents a novel framework for technological enablers of Sustainable Digital Finance, integrating Finance 5.0 with emerging technologies using the ADO-TCM framework. It addresses gaps in quantum computing, ethical AI, and DeFi-driven climate finance, offering insights for policymakers, financial institutions, and academia in fostering resilient and sustainability-driven financial ecosystems.

Zefeng Bai, Shuxin Zheng, Miaoqing Jia

Purpose This study investigates whether individuals facing financial constraints, as indicated by the use of alternative financial services (AFS), are more likely to invest in cryptocurrencies, potentially using the new financial instrument as an alternative means to alleviate their financial stress. Additionally, we examine whether financial education moderates this relationship. Design/methodology/approach Using data from the 2021 National Financial Capability Study (NFCS), we employ an ordinary least squares (OLS) regression to examine the relationship between cryptocurrency investment and AFS use. Furthermore, we implement the propensity score matching (PSM) analysis and an instrumental variable (IV) approach to reduce potential endogeneity concerns. Finally, we incorporate interaction terms between financial education and AFS use in the OLS model to assess the moderating effect of financial education. Findings We find that AFS use relates to a significantly higher likelihood of participating in cryptocurrency investment, suggesting that people under financial constraints are more inclined to invest in cryptocurrencies. This could be attributed to the potential of cryptocurrencies to generate substantial returns, where people under financial stress see them as an opportunity to improve their financial condition. This is further supported by an increased propensity of participating in cryptocurrency investment associated with job loss due to the pandemic. Finally, we also find that financial education negatively moderates the linkage between financial constraints and cryptocurrency investment. Originality/value This study provides novel insights into the behavioral drivers of cryptocurrency investment, particularly under financial constraints and demonstrates the heterogeneous effect of financial education. By bridging gaps in the literature on personal finance and cryptocurrency markets, the study offers valuable implications for financial education and policies that could help maintain individual financial stability in times of economic downturns.