Search Results (555)

In this study, we propose a structured valuation framework for non-fungible tokens (NFTs), a distinct class of digital assets whose pricing mechanisms remain insufficiently understood. Based on previous empirical studies and illustrative case analyses of three major NFT collections, we synthesize insights from non-cash-flow asset theory, market microstructure, and behavioral finance to construct a four-layer valuation framework consisting of the Asset, Market, Technology, and Ecosystem layers. We identify three NFT-specific mechanisms—verified digital scarcity, pseudonymous signaling, and on-chain herding—that modify or extend traditional valuation paradigms. Empirical evidence from the literature suggests that rarity-driven asset features and social-influence dynamics are dominant price determinants, while wash trading, fragmented liquidity, and platform incentive structures generate persistent distortions in price discovery. Case analyses of CryptoPunks, Bored Ape Yacht Club, and Pudgy Penguins demonstrate how differing risk exposures across the four layers translate into distinct valuation trajectories. With this framework, we obtain a basis for improved risk assessment, regulatory oversight, and business model design in NFT markets. Full article

Banks around the globe are rapidly progressing towards the adoption of digital currency. However, its adoption rate has been consistently low among both emerging and advanced economies. This study examines the user adoption of the Indian digital currency, the e-Rupee, based on a primary survey conducted between July 2025 and September 2025 of 751 respondents. The study adopted a blend of TAM and nudge theory for the first time in the digital currency domain, using the stated preference method in finance literature to understand the willingness to shift to the e-Rupee in India. Using binary logit regression, we test two hypotheses. The results show that apart from socioeconomic predictors, adoption of the e-Rupee is significantly influenced by digital financial literacy. With respect to the willingness to shift to the e-Rupee, the study found TAM constructs like perceived convenience and perceived belief in the study as the key predictors. Unlike the current literature, our study finds that trust is not a significant predictor of e-Rupee adoption. This highlights the credibility of the central bank of the country and the future growth of its digital currency. The findings highlight the importance of digital financial literacy and behavioral intentions, rather than technical viability, as the key factors in digital currency adoption in India. Full article

This study investigates the relationship between digital financial literacy (DFL) and hyperbolic discounting among 104,993 active securities account holders in Japan. As digital financial services expand rapidly, individuals increasingly require not only traditional financial knowledge but also the capacity to understand digital platforms, evaluate online financial information, and manage emerging technological risks. Using data from the 2025 wave of the Survey on Life and Money, hyperbolic discounting is measured through intertemporal monetary choice scenarios, while DFL is constructed as a multidimensional index encompassing digital knowledge, financial knowledge, service awareness, attitudes, behaviors, practical capability, and protection against digital fraud. Probit regression results reveal a statistically significant negative association between DFL and hyperbolic discounting, indicating that individuals with stronger digital financial competencies are less likely to exhibit hyperbolic discounting. Attitudinal components of DFL exhibit the strongest effects, suggesting that internalized financial beliefs may play a more decisive role than technical knowledge in promoting time-consistent decision-making. Subsample analyses further highlight gender-differentiated patterns in demographic and economic influences on present bias. These findings contribute to behavioral finance by integrating digital capability into intertemporal choice research and provide policy-relevant implications for designing comprehensive financial education and digital literacy initiatives in increasingly digitalized financial environments. Full article

Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study ($N=25$, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is $\beta =0.0036$ ($p=0.495$) and the Alphabet Rank coefficient is $\beta =-0.0027$ ($p=0.642$), with the results robust to all tested parameterizations ($\lambda \in [0.05,0.20]$; $p>0.50$ throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower. Full article

This study examines the relationship between financial risk tolerance and organizational resilience among small business managers in the Brazilian Northeast, a region strongly affected by economic fragility and intensified uncertainty during and after the COVID-19 pandemic. Using a positivist, quantitative, cross-sectional design, data were collected from 218 managers through validated scales of financial risk tolerance and organizational resilience and analyzed using confirmatory factor analysis, cluster analysis, ANOVA, and correlation techniques. Results indicate that most managers exhibit medium to high financial risk tolerance and that higher tolerance is positively associated with greater organizational adaptability, especially in dimensions related to teamwork, knowledge sharing, and leadership. In contrast, no significant association was found between financial risk tolerance and organizational planning capacity, suggesting that planning routines operate independently of individual risk attitudes. The findings underscore the role of behavioral characteristics in shaping resilience and highlight innovation, internal resources, and leadership as critical factors supporting organizational adaptation in resource-constrained environments. This study contributes to the limited empirical literature connecting behavioral finance and organizational resilience in emerging economies and offers practical implications for strengthening entrepreneurial training and resilience culture in small firms. Future research should expand geographic coverage and explore team-level perspectives and mixed-method approaches. Full article

The deployment of green hydrogen production is increasingly considered a strategic opportunity for energy-exporting countries. However, beyond technological and environmental aspects, large-scale industrial projects may generate complex and uncertain social and economic impacts at the regional level. This study investigates the potential social implications of introducing a green hydrogen production plant in the Department of Paysandú, Uruguay, using a System Dynamics modeling approach. It proposes an initial system model designed to establish a foundational Modeling and Simulation framework. The model explicitly represents feedback mechanisms linking public finance, education, labor competencies, productivity, and social behavior impact, allowing the exploration of long-term socio-economic trajectories under alternative institutional and policy conditions. It is used as an exploratory decision-support tool to assess conditional pathways, trade-offs, and risks. Results indicate that positive social outcomes, such as human capital accumulation and regional income growth, are possible but not automatic; they depend critically on governance capacity, fiscal sustainability, labor market coordination, and social acceptance, and may be attenuated or delayed under adverse scenarios. While this framework provides a strategic engineering lens on the social dimension, it represents a first step toward a comprehensive decision-making tool. The study analyzes a complex system by integrating energy, production, economic, social, and environmental aspects from strategic engineering lens and contributes to the literature by integrating social dimension and institutional constraints into a Modeling and Simulation framework applied to green hydrogen industrialization, offering insights into policy design under uncertainty in emerging energy-export contexts. Full article

Fiscal and tax new media has emerged as a new channel for government-enterprise engagement, linking policy communication with firms’ sustainability-oriented decisions. This study hand-collects the launch status of official microblog accounts for finance and taxation departments in China’s prefecture-level cities. This paper combines these data with firm-level observations on China’s green enterprises from 2008 to 2022, and clearly defines the sample of green enterprises. Defining the sustainable spirit among green entrepreneurs from the perspective of entrepreneurship and innovation. This is to estimate how government communication and policy signaling shape firms’ sustainability-oriented behavior. Treating the introduction of official fiscal and tax new media as a quasi-natural experiment, we apply a staggered difference-in-differences design to identify its effect on green entrepreneurs’ sustainable spirit. The study finds that launching official fiscal and tax new media significantly stimulates the sustainable spirit of green entrepreneurs. Mechanism tests suggest that the effect operates through improvements in information infrastructure and governance capacity, including higher internet penetration, reduced fiscal and tax irregularities, and stronger digital governance. Particularly in regions with weaker government–business relations, more integrated administrative systems, lower fiscal pressure, and higher government subsidies, the promoting effect is more significant. Overall, the findings offer policy implications for strengthening the effectiveness of public digital communication and for fostering green entrepreneurs’ sustainable spirit. Full article

This study investigates the impact of sustainability-related uncertainty (SRU)—captured via the Sustainability-related Uncertainty Index in equal-weighted (ESGUI_EQ) and GDP-weighted (ESGUI_GDP) forms—on the volatility of green financial assets, focusing on decentralized finance (DeFi) protocols and Environmental, Social, and Governance (ESG)-focused Exchange-Traded Funds (ETFs). Employing a fuzzy logic framework, complemented by 3D surface visualization, Rule Viewer analysis, diagnostic validation, and Granger causality tests, the study uncovers non-linear, asymmetric, and time-varying responses of these assets to sustainability ambiguity. Empirical results reveal a structural divergence: DeFi protocols amplify volatility due to fragmented governance, speculative investor behavior, and sensitivity to policy-driven signals, often exhibiting bidirectional predictive feedback with SRU, whereas ESG ETFs maintain stability through diversification, regulatory oversight, and rigorous ESG screening, primarily absorbing sustainability shocks. These findings extend sustainable finance theory by integrating governance, technology, and policy dimensions, and illustrate the value of fuzzy logic combined with Granger causality in modeling complex, ambiguous markets. From a practical standpoint, the study provides actionable guidance for investors, fund managers, and policymakers, emphasizing the importance of technology-informed governance, standardized ESG disclosures, regulatory sandboxes, and continuous monitoring of SRU. Full article

The rapid rise in cryptocurrencies has created an investment environment marked by unprecedented levels of information volume, fragmentation, and volatility. While prior research has examined drivers of trust and adoption in crypto markets, far less is known about the psychological consequences of information overload on investor decision-making. This study addresses this gap through nineteen semi-structured interviews with individual cryptocurrency investors, analyzed using an inductive, manually conducted thematic approach. Findings reveal four interconnected dynamics: decision fatigue and paralysis, heuristic reliance on influencers and peers, emotional strain characterized by anxiety and fear of missing out (FOMO), and diverse coping strategies ranging from selective filtering to withdrawal. These results demonstrate that crypto investing is not only a financial process but also a cognitively and emotionally taxing experience. By linking investor narratives to broader theories of decision fatigue, bounded rationality, and consumer vulnerability, the study contributes to interdisciplinary debates in marketing, behavioral finance, and consumer psychology. Practically, the findings highlight the need for clearer communication strategies, supportive platform design, and financial education initiatives that help investors manage cognitive strain and decision fatigue. In a market where credibility is fluid and decisions are often made under conditions of overload, understanding the psychological dimensions of investment behavior is essential. Full article

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers. Full article

Economic crises significantly restrict corporate access to external financing, and regional differences in recovery capacity deserve close attention. This study examines the financial structure and debt of large enterprises in the Visegrád Four (V4) countries (Hungary, Czechia, Poland, Slovakia), focusing on firms with annual revenues above €10 million. Using data from 2021 to 2023, the analysis explores the relationship between corporate debt—including total debt and loan volumes—and regional economic characteristics at the NUTS 3 level. Financial indicators are assessed in comparison with regional productivity data and a sector-specific specialization index sourced from Eurostat. The analysis targets the post-COVID-19 recovery period, which significantly influenced corporate financial behavior. The results indicate that corporate debt increased sharply at the onset of the COVID-19 pandemic and subsequently declined, while remaining strongly concentrated in capital regions. Higher firm concentration and employment scale are associated with greater regional indebtedness, whereas stronger productive capacity is linked to lower reliance on external debt outside metropolitan cores. Overall, the findings highlight pronounced structural and regional heterogeneity, illustrating how spatial concentration and underlying regional characteristics shape corporate debt dynamics during periods of economic stress. Full article

Federated learning (FL) has emerged as a transformative distributed learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy across large, diverse, and geographically dispersed clients. Despite its rapid adoption in mobile networks, Internet of Things (IoT) systems, healthcare, finance, and edge intelligence, FL continues to face several persistent and interdependent challenges that hinder its scalability, efficiency, and real-world deployment. In this survey, we present a systematic examination of six core challenges in federated learning: heterogeneity, computation overhead, communication bottlenecks, client selection, aggregation and optimization, and privacy preservation. We analyze how these challenges manifest across the full FL pipeline, from local training and client participation to global model aggregation and distribution, and examine their impact on model performance, convergence behavior, fairness, and system reliability. Furthermore, we synthesize representative state-of-the-art approaches proposed to address each challenge and discuss their underlying assumptions, trade-offs, and limitations in practical deployments. Finally, we identify open research problems and outline promising directions for developing more robust, scalable, and efficient federated learning systems. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking a unified understanding of the fundamental challenges shaping modern federated learning. Full article

Emerging digital technologies are increasingly embedded in consumer-facing financial services, reshaping how users experience, evaluate, and engage with AI-mediated interactions. This paper investigates how the perceived sociality of AI Digital Human Advisors influences user experience in digital financial services. Sociality—defined as the extent to which users perceive an AI Digital Human Advisor as a socially capable actor (e.g., responsive, relational, and role-embedded) rather than a purely functional tool—was experimentally manipulated across four controlled behavioral experiments simulating interactions on financial platforms. The results from four controlled experimental simulations consistently demonstrate that, under controlled interaction conditions, high-sociality AI advisors significantly enhance both utilitarian and hedonic value. Social presence was found to partially mediate these effects, revealing the psychological mechanism through which social cues embedded in emerging AI technologies are transformed into experiential value. Furthermore, two boundary conditions were identified: communication style and usage context. Communication framed around task completion amplified the influence of sociality on utilitarian value, whereas interaction styles emphasizing social connection strengthened its effect on hedonic value. Likewise, purchase-related scenarios heightened functional perceptions, while browsing situations elicited stronger emotional responses. By situating AI Digital Human Advisors within the broader context of emerging digital technologies, these findings extend Social Response Theory into AI-mediated financial environments and provide insights into how technologically enabled social cues shape consumer experience and behavior in digital finance. Full article

Cryptocurrency trading presents significant challenges due to extreme market volatility, rapid regime transitions, and non-stationary dynamics that render traditional trading strategies ineffective. Existing reinforcement learning approaches for cryptocurrency trading typically employ simplistic profit-based reward functions that fail to adequately capture risk management considerations, market microstructure costs, temporal dependencies, and regime-specific optimal behaviors. This limitation often results in strategies that perform well during favorable market conditions but suffer catastrophic losses during downturns. This paper introduces five novel reward functions grounded in economic utility theory, market microstructure, behavioral finance, adaptive risk management, and regime-conditional optimization. We systematically evaluate these reward functions across three reinforcement learning algorithms (Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic) and four distinct market regimes (bull, bear, high volatility, and recovery), using Bitcoin hourly data from 2018–2022. Our comprehensive experimental evaluation demonstrates that the Adaptive Risk Control reward function achieves exceptional performance, with a Sharpe ratio of 2.47, cumulative return of 26.4%, and maximum drawdown of only 16.8% during the predominantly bearish 2022 test period. Critically, regime-specific analysis reveals substantial performance heterogeneity: Adaptive Risk Control excels during high volatility (Sharpe ratio 3.21), while Temporal Coherence and Asymmetric Market-Conditional rewards dominate in trending and bear markets, respectively. These findings establish that sophisticated, theory-grounded reward engineering—rather than algorithmic innovations alone—constitutes the primary lever for improving RL trading systems, enabling positive risk-adjusted returns even during severe market downturns. Full article

As cross-border e-commerce expands in emerging economies, consumer participation increasingly depends on perceived transaction risk linked to digital payments, settlement, dispute resolution, and institutional enforceability. This study reconceptualizes online purchase intention (OPI) as a decision embedded in retail international finance. Extending the Theory of Planned Behavior (TPB), it integrates Internet Trustworthiness Behavior (ITB) and Country of Origin (COO) as risk-relevant signals shaping consumer judgment under cross-border uncertainty. Survey data from 390 digitally active consumers in the Dominican Republic were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that ITB strengthens perceived behavioral control, attitudes toward online purchasing, and subjective norms, while also exerting a direct positive effect on OPI. COO emerges as a strong direct predictor of OPI, functioning as a heuristic indicator of country credibility when formal safeguards appear weak. Contrary to standard TPB expectations, perceived behavioral control negatively predicts OPI, suggesting that greater digital competence may heighten awareness of expected losses and limited recourse in high-risk environments. The findings advance international business and finance research by showing how micro-level trust practices and macro-level country signals jointly shape consumer risk management in cross-border digital markets, with implications for inclusive participation and consumer protection. Full article