Search Results (115)

This study aims to explore the relationship between the digital financial literacy of homestay business owners and economic sustainability. A conceptual model is developed by integrating three primary constructs—performance expectancy, effort expectancy, and facilitating conditions—from the unified theory of acceptance and use of technology (UTAUT) with digital financial literacy and FinTech use by homestay business owners. Further, the effect of FinTech use on economic sustainability is examined through the interaction between facilitating conditions and financial inclusion. Data were collected from Southern India, and hypothesized relationships were tested after checking the measurement properties of the survey instrument. The findings indicate that (i) the digital financial literacy of homestay business owners is a precursor to FinTech use, which, in turn, is positively associated with economic sustainability; (ii) digital financial literacy interacting with performance expectancy (first moderator) and effort expectancy (second moderator) significantly influenced FinTech use; and (iii) FinTech use interacting with facilitating conditions (first moderator) and financial inclusion (second moderator) increased economic sustainability. The three-way interactions in this study provide insights into the boundary conditions that increase FinTech use and economic sustainability, particularly in the context of homestay businesses. The proposed digital financial literacy and FinTech adoption model contributes to the information technology adoption research by extending the UTAUT, in which performance expectancy and effort expectancy play a vital role in FinTech adoption by homestay business owners. The three-way model developed and tested, to the best of our knowledge, is the first of its kind in the Indian context and hence makes a pivotal contribution to the advancement of the UTAUT model through its application to homestay business owners. The implications for theory and practice are discussed. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

Experiments on hypersonic boundary-layer instability of a fin–cone configuration were conducted in a Φ 0.5 m Mach 6 Ludwieg tube tunnel. Infrared thermography and high-frequency pressure sensors were used to measure the transition front and instability waves under four different nose bluntness conditions. On the leeward surface, transition is delayed near the centerline due to expansion waves generated by the double-cone structure. The region close to the corner is strongly influenced by the horseshoe vortex, whereas instability waves around 110 kHz manifest as the flow moves away from it. In contrast, transition on the windward surface occurs earlier and broadband high-frequency instability waves of 160–300 kHz are present near the corner. Increasing nose bluntness strongly suppresses transition away from the fin root, especially near the centerline and on the fin-off cone side, but has a relatively limited impact on the shock-interaction regions near the fin–cone corner. Transition on the fin surface remains insensitive to nose bluntness variations. This work elucidates the distinct transition behaviors across different regions of a complex fin–cone configuration and their differential responses to nose bluntness, providing valuable insights for the aerodynamic design and transition prediction of hypersonic vehicles. Full article

The problem of shock wave/boundary layer interaction induced by compression corners widely exists in the external and internal flows of various supersonic/hypersonic aircraft. In practical engineering applications, multistage continuous compression is often used in the fin/rudder structure, while in internal flow, multistage compression schemes are usually employed at the inlet to enhance total pressure recovery; therefore, it is necessary to investigate the characteristics of multistage compression corner shockwave/boundary layer interactions. In basic research, it is usually simplified as the double compression corner shockwave/boundary layer interaction issue. In this paper, an experimental study of hypersonic shock/boundary layer interaction characteristics is conducted under quiet and noise inflow conditions, respectively, for the double compression corner model. Using high-speed Schlieren, the typical structure of shockwave/shockwave interaction and shockwave/boundary layer interaction above the corner is explored under both quiet and noisy incoming flow conditions. Then, based on gray average, root-mean-square analysis, Fast Fourier transform, proper orthogonal decomposition, and dynamic mode decomposition methods, the time-average and unsteady characteristics of the double compression corner configuration-induced separation were studied, and a comparative analysis was conducted. The difference law between wind tunnel noise level and interaction characteristics was summarized. Finally, the characteristic length and spectral characteristics of unstable waves that dominated the stability of the plate boundary layer were studied. The formation mechanism of separation is discussed, which provides technical support for the internal and external aerodynamic design and targeted optimization of hypersonic vehicles. Full article

Proton exchange membrane fuel cells (PEMFCs) represent a new generation of clean and efficient energy conversion devices, demonstrating broad application prospects in transportation, distributed power generation, and other fields. The geometric configuration of the cathode gas channel (GC) and the surface microstructure of the gas diffusion layer (GDL) are core factors influencing the efficiency of reactant gas transport and water management performance. However, conventional rectangular flow channels suffer from insufficient convective enhancement and restricted oxygen supply beneath the fins. Furthermore, homogeneous GDLs exhibit limited diffusion and drainage capabilities, often leading to oxygen depletion and flooding downstream of the cathode, significantly limiting overall cell performance. To address these challenges, this study designs a novel centrally positioned fin-type barrier block. A three-dimensional multiphysics numerical model integrating GDL surface microporosity with the internal barrier block flow channels is constructed to systematically investigate the synergistic mechanisms of microporous topology and flow channel structure on two-phase flow distribution, oxygen mass transfer, and electrochemical performance. The results demonstrate that this model accurately captures the dynamic evolution of flow fields within the GDL. Compared to conventional structures, significant coupling effects exist between the GDL microporous structure and the novel barrier block. Their synergistic interaction forms multi-scale mass transfer enhancement and dewatering pathways, providing quantifiable optimization pathways and structural parameter references for high-power-density PEMFC cathode design. Full article

Fish display remarkable swimming capabilities through the coordinated interaction of the body and caudal fin, yet the potential role of a passively pitching tail in enhancing hydrodynamic performance remains unresolved. In this work, we evaluate the performance of a carangiform swimmer equipped with either an actively pitching tail or a passively pitching tail. High-fidelity fluid–structure interaction simulations are employed to assess how variations in joint stiffness, damping, and inertia influence thrust generation, power demand, and overall stability at two representative Reynolds numbers, 500 and 5000. The results reveal that actively pitching tails tend to generate greater thrust, while passively pitching tails deliver improved outcomes in terms of power demand at the lower Reynolds number. Larger pitching amplitudes contribute positively only when associated with higher swimming frequency; when produced by reduced inertia or more flexible joints, they lead to unfavorable effects. At the higher Reynolds number, active tails consistently outperform passive ones, although a small subset of passive cases still achieve favorable performance. Across all cases, a recurring balance emerges, with thrust production and power expenditure varying inversely. These findings clarify the hydrodynamic consequences of passive versus active tail motion and establish design principles for bio-inspired underwater vehicles, in which smaller swimmers may benefit from passive tail pitching, whereas larger swimmers are better served by active control. Full article

Financial fraud detection is critical to modern economic security, yet remains challenging due to collusive group behavior, temporal drift, and severe class imbalance. Most existing graph neural network (GNN) detectors rely on pairwise edges and correlation-driven learning, which limits their ability to represent high-order group interactions and makes them vulnerable to spurious environmental cues (e.g., hubs or temporal bursts) that correlate with labels but are not necessarily causal. We propose Causal-DHG, a dynamic hypergraph framework that integrates hypergraph modeling, causal intervention, and multi-view contrastive learning. First, we construct label-agnostic hyperedges from publicly available metadata to capture high-order group structures. Second, a Multi-Head Spatio-Temporal Hypergraph Attention encoder models group-wise dependencies and their temporal evolution. Third, a Causal Disentanglement Module decomposes representations into causal and environment-related factors using HSIC regularization, and a dictionary-based backdoor adjustment approximates the interventional prediction $P(Y\mid do(C\left)\right)$ to suppress spurious correlations. Finally, we employ self-supervised multi-view contrastive learning with mild hypergraph augmentations to leverage unlabeled data and stabilize training. Experiments on YelpChi, Amazon, and DGraph-Fin show consistent gains in AUC/F1 over strong baselines such as CARE-GNN and PC-GNN, together with improved robustness under feature and structural perturbations. Full article

Purpose: The central research question of the study is how objective financial knowledge and subjective financial confidence interact and relate to digital financial behavior and the use of FinTech tools. By examining both objective knowledge refers to measured, test-based financial competence and subjective confidence denote self-assessed financial understanding, the research offers insight into the psychological and demographic drivers of FinTech use and perceived financial well-being. Design/methodology/approach: Based on the OECD’s 2023 international financial literacy survey, the study uses a nationally representative Hungarian sample. It employs non-parametric statistical methods, linear regression, and two-step cluster analysis. Three composite indicators, general digital activity, digital financial engagement frequency, perceived financial security were developed to measure general digital activity, frequency of digital financial engagement, and perceived financial security. Findings: Results reveal a moderate but significant correlation between actual and self-assessed financial knowledge. Men score higher on both measures, though self-assessment bias does not significantly differ by gender. Higher education and income levels are associated with stronger financial literacy and more frequent use of FinTech tools, while age correlates negatively. However, the accuracy of self-perception is not explained by these demographic factors. Cluster analysis identifies four distinct financial knowledge profiles and five consumer digital behavior types, revealing disparities in digital financial inclusion and confidence. Originality: This research contributes a multidimensional perspective on how consumer capabilities, attitudes, and digital behavior influence FinTech adoption. By integrating behavioral, demographic, and psychological factors, the study offers practical implications for targeted financial education and the design of inclusive, human-centered digital financial services—especially relevant for emerging European markets. Full article

This study synthesized an efficient amino-modified silica gel decolorizer by aminating silica hydroxyls, characterized via SEM, FT-IR, N₂ adsorption and XPS. It investigated its decolorization of Sparassis crispa crude polysaccharides using decolorization rate and polysaccharide retention as indices, revealing pigment adsorption mechanisms. Polysaccharide activity preservation by physical adsorption was evaluated via antioxidant (DPPH, ABTS, •OH scavenging) and zebrafish caudal fin repair assays. The results showed 79.85% decolorization and 86.54% polysaccharide retention within 5 min, with over 75% decolorization after five cycles of reuse. Results suggest that pigment adsorption may involve interactions between amino groups and chromophoric compounds. The decolorized polysaccharides showed better antioxidant and zebrafish caudal fin repair activities than those treated with traditional H₂O₂. These findings support the development of efficient, low-damage decolorization strategies for edible and medicinal polysaccharides. Full article

This study examines the association between Saudi banks’ internal adoption of AI-enabled FinTech-related digital tools and their financial performance, sustainability performance, and financial stability over the period 2015–2024. Using a panel dataset of 10 banks, the analysis investigates how the adoption of AI-driven technologies—such as machine-learning credit assessment, robo-advisory systems, and automated compliance tools—is related to market performance (Tobin’s Q), accounting performance (ROA and ROE), financial stability (Z-Score), and sustainability outcomes measured by both Bloomberg ESG Disclosure Score and the LSEG ESG performance-oriented score. To ensure robust inference and reduce simultaneity concerns, the empirical strategy employs Pooled OLS and Fixed Effects Models with Driscoll–Kraay standard errors, as well as a dynamic Fixed Effects Models incorporating lagged dependent variables, lagged independent variables, and shock-interaction terms. Bank-specific characteristics—including size, age, leverage, liquidity, loan-to-deposit ratio, non-performing loans, net interest margin, market capitalization, and board size—are included as controls. The findings indicate a positive and statistically significant relationship between banks’ internal adoption of AI-enabled digital/FinTech-related technologies and their financial performance, sustainability performance, and financial stability. These relationships remain robust across estimation approaches, providing insights for policymakers, regulators, and bank managers seeking to advance digital transformation while safeguarding financial soundness and supporting sustainable development in the Saudi banking sector. Full article

Using Chinese A-share listed firms from 2011 to 2023, this study develops and tests a dual-engine framework in which digital transformation and green technological innovation constitute two core transmission channels through which digital finance improves corporate ESG performance. Based on the FinTech Innovation Regulatory Pilot Policy in China, we implemented a staggered DID model for causal identification. Then, we further conducted a series of robustness checks, including Bartik IV, to address residual endogeneity concerns. We found that (1) digital finance can enhance corporate ESG performance, with particularly strong effects on the environmental and governance dimensions. (2) Digital transformation and green technological innovation are the primary mechanisms through which digital finance improves ESG performance. (3) The interaction between digital transformation and green technological innovation forms mutually reinforcing “dual engines” that amplify the benefits of digital finance for ESG performance. (4) Higher institutional investors’ shareholding ratio strengthens the positive effect of digital finance on corporate ESG performance, consistent with the role of external governance. (5) The enabling effect of digital finance is more pronounced among firms in the introduction, growth, and maturity stages of the corporate lifecycle, as well as among firms located in eastern and central regions and in non-heavy-polluting industries. This study uncovers the internal logic by which digital finance advances corporate sustainability through technological upgrading and environmental innovation, and it provides theory-driven and empirically grounded evidence for building integrated ESG governance frameworks. The results offer actionable insights for firms worldwide pursuing the twin goals of digitalization and green development under carbon neutrality targets. Full article

This study investigates the influence of banking service quality and customer trust on customer retention behavior, considering the mediating role of customer satisfaction and the moderating role of FinTech. In light of the growing digitalization in the banking sector, the study aims to understand how these constructs interact to drive long-term customer loyalty. A quantitative research approach was adopted using data collected through a structured questionnaire administered to banking customers. The relationships among variables were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM), assessing both direct and indirect effects. The results show that banking service quality and customer trust significantly enhance customer satisfaction, which in turn positively influences customer retention behavior. Moreover, satisfaction was found to mediate the relationships between both service quality and trust with retention. FinTech demonstrated a strong direct effect on retention and also significantly moderated the satisfaction–retention link, amplifying its impact when FinTech services are effectively utilized. This study contributes to the relationship marketing literature by introducing FinTech as a novel moderating variable in the satisfaction–retention framework. It offers practical insights for banks aiming to enhance retention by improving service quality, fostering trust, and leveraging digital technologies to strengthen customer relationships. Full article

The rapid digital transformation of the financial sector has driven supervisory authorities to develop new tools for engaging with fintech innovation. Among these, Innovation Hubs have become essential mechanisms for improving regulatory dialogue, interpretive clarity, and institutional learning. This article examines the Romanian Fintech Innovation Hub (FIH), launched by the National Bank of Romania (NBR) as a consultative platform to support fintech and payment service providers operating within complex legal environments. Using a qualitative, single-case methodology (2019–2023), the study draws on internal NBR documentation, anonymized supervisory materials, and interviews with fintech founders, oversight officers, and policy specialists. The analysis evaluates the Hub’s performance across five key dimensions: stakeholder engagement, regulatory learning, policy calibration, innovation barriers, and institutional reflexivity. Findings reveal that while the Hub strengthened supervisory understanding and enhanced trust-based interaction, its influence on rulemaking and market access was limited by structural and procedural constraints, including resource gaps and the absence of a regulatory sandbox function. Nonetheless, the Romanian experience demonstrates how institutional learning can emerge even in bank-dominated markets, generating internal adaptation and improving fintech compliance readiness. Comparative insights from Hungary and Italy highlight the advantages of modular, risk-proportionate engagement models that integrate advisory and testing functions. The study contributes to the theory of adaptive regulation by proposing that innovation hubs function as feedback loop mechanisms linking market experimentation with supervisory evolution, offering a replicable model for small and emerging financial systems seeking to balance innovation facilitation with prudential soundness and legal certainty. As such, it provides generalizable insights for central banks and government policymakers on developing FinTech hubs that balance innovation facilitation with prudential soundness and legal certainty. Full article

With the growing prominence of large technology firms and the shift in news dissemination driven by social media, scholars have increasingly examined how public discourse about these companies shapes financial markets. Focusing on Apple, Amazon, and Microsoft during the transitional period of January 2015–January 2020, this study evaluates attention and sentiment across traditional news media, social media, and web search in relation to stock market outcomes. We use relatively fine-grained weekly data to link media attention and sentiment to stock returns, volatility, and trading volume. To compare media sentiment across sources, we apply FinBERT-based sentiment analysis, drawing on advances in domain-specific language modeling tailored to financial texts. Results show that social media sentiment (Twitter), exerts a consistently positive and significant influence, while the effects of traditional news media (New York Times) and web search activity (Google Trends) are more irregular. The impact also varies across firms: Twitter sentiment is strongly related to trading volume and volatility for Amazon and Microsoft, but appears less influential for Apple, whose large trading base may dilute the effect. These findings offer a historical baseline for media–finance interactions and highlight how text analysis illuminates the pre-COVID era of big technology firms. Full article

Amid rapid industrialization and the growing integration of financial technologies, emerging economies face increasing pressure from rising ecological footprints (ECOF). This study examines the environmental impacts of natural resource rents (NRES) and digital financial technology (DFIN), emphasizing the moderating role of governance (INST), using data from the top 10 emerging economies between 1995 and 2023. The Method of Moments Quantile Regression (MMQR) approach is employed to capture heterogeneous effects across different levels of environmental stress. The results reveal that both NRES and DFIN exacerbate ECOF, particularly in economies facing higher ecological pressures. However, strong governance significantly reduces these adverse effects, especially at higher ECOF quantiles, highlighting its pivotal role in aligning resource management and digital innovation with environmental sustainability goals. Interaction terms further confirm that effective institutional quality can buffer the ecological risks associated with resource exploitation and FinTech expansion. Additionally, Dumitrescu–Hurlin panel causality tests reveal a unidirectional causality from NRES and economic growth (EGRO) to ECOF, while bidirectional relationships are observed between DFIN, INST, education, urbanization, renewable energy, and ECOF. These findings underscore the complex interlinkages between economic growth, technological advancement, and institutional frameworks. In the context of post-COP28 climate commitments and Sustainable Development Goals, this study provides timely policy recommendations to promote sustainable growth through robust governance, responsible resource utilization, and balanced FinTech integration. Full article