Search Results (128)

Background: Digital transformation has changed how patients interact with hospitals, making online supplementary services an important part of perceived service quality and relationship outcomes. This study examined the association between cyberspace supplementary health services (CSS), conceptualized as the Online Flower of Service (OFOS), and patient loyalty in private hospitals in Jeddah, Saudi Arabia, and evaluated whether patient experience mediates this relationship. Methods: A cross-sectional survey was conducted among adult inpatients and outpatients who had used at least one online hospital service. Data were collected electronically between 10 February 2026 and 10 May 2026 using a structured Arabic questionnaire administered via Google Forms. The final sample included 730 complete responses. CSS was measured across five digital supplementary dimensions (E-Information, E-Order Taking, E-Consultation, E-Billing, and E-Payment), alongside patient experience and patient loyalty, using five-point Likert scales. The measurement model was evaluated using confirmatory factor analysis, and the structural relationships were tested using structural equation modeling. Results: CSS demonstrated a significant positive association with patient loyalty (β = 0.282, p < 0.001) and a strong positive association with patient experience (β = 0.591, p < 0.001). Patient experience was also positively associated with patient loyalty (β = 0.431, p < 0.001), supporting its mediating role in the CSS–patient loyalty relationship. Conclusions: Digitally delivered CSS components were positively associated with patient loyalty, with patient experience serving as an important mediating mechanism. Strengthening online information access, appointment-related processes, digital consultation, billing transparency, and payment convenience may be associated with more favorable patient experiences and stronger loyalty intentions in private hospital settings in Saudi Arabia. Full article

Automatic License Plate Recognition (LPR) is an essential technology in modern intelligent transportation systems, facilitating the identification of vehicles without manual intervention. It supports a wide range of applications, including traffic monitoring, electronic toll payment, parking automation, secure access management, and law enforcement operations, thereby improving transportation efficiency, security, and operational effectiveness. However, real-world license plate images are frequently affected by motion blur, noise, illumination variations, adverse weather conditions, and low resolution, which significantly degrade recognition performance. To address these challenges, this study proposes an Adaptive Hybrid CNN–Mamba–Multi-Head Attention Network with Northern Goshawk Optimization (AHCMM-Net-NGO) for robust LPR. The proposed framework combines license plate detection, progressive image restoration and enhancement, hierarchical multi-scale feature learning, efficient contextual modeling using Vision Mamba, adaptive attention refinement, and automatic hyperparameter optimization within a unified end-to-end architecture. The framework was evaluated on the UFPR-ALPR dataset containing 4500 fully annotated vehicle images captured under real-world driving conditions. Experimental outcomes demonstrate superior recognition performance, achieving 98.96% accuracy, 98.89% precision, 98.81% recall, and a 98.85% F1-score. Comprehensive experimental evaluations, including ablation studies, hyperparameter sensitivity analysis, cross-validation, and comparative performance analysis, further demonstrate the efficacy, robustness, and generalization capability of the proposed framework. Overall, the proposed AHCMM-Net-NGO framework provides an accurate, reliable, and robust solution for license plate recognition under challenging imaging conditions and demonstrates strong potential for intelligent transportation systems, although practical deployment should consider the computational requirements of the integrated deep learning framework. Full article

This study examines whether recognized intangible assets carry information about bank performance in an emerging market, and whether their information value is conditioned by the maturity of macro digital infrastructure. Using a balanced panel of 28 Indonesian commercial banks over 2015–2024 (280 firm-year observations), we estimate two-way fixed-effects models with macro digital infrastructure, an economy-wide principal component index of internet penetration, mobile and broadband subscriptions, and electronic payment volume as a cross-layer moderator. Intangible investment intensity, proxied by the ratio of reported intangible assets to total assets, shows weak direct associations with performance; only the operating efficiency ratio displays a marginally significant short-run cost, consistent with transition-cost dynamics. The central result is conditional: the interaction between intangible intensity and macro digital maturity is strongly significant for operating efficiency (β = −2.587, p = 0.005), with the implied efficiency cost contracting by a model-implied 88 percent across the observed range of digital maturity (an estimate computed from the estimated coefficients over the observed sample variation, not a structural causal magnitude). Heterogeneity is pronounced across regulator-defined bank tiers (KBMI): the four largest banks realize positive profitability effects, whereas mid-tier banks bear transition costs. Results are robust to Driscoll–Kraay standard errors, system GMM, sub-sample splits, and outlier exclusion. The findings show that the information value of recognized intangibles in banking is state-contingent, extending the intangible-asset and digitalization literature to emerging-market banking. Full article

This paper examines how value-added tax (VAT) reforms affected recorded point-of-sale (POS) spending in Saudi Arabia’s restaurant, café, and food service sector during a period of rapid payment digitalization. Two policy shocks are analyzed: the introduction of a 5% VAT in January 2018 and the increase to 15% in July 2020. Using monthly official POS data from January 2016 to January 2024, the study applies an interrupted time-series framework. Baseline estimates are obtained using Generalized Least Squares (GLS) with AR (1) correction. In contrast, seasonal SARIMAX and Error Correction Model (ECM) specifications are used as robustness checks and to distinguish short-run from long-run dynamics. Controls include food and beverage price indices, headline inflation, and COVID-19 disruptions. Results show statistically significant positive level shifts in recorded POS sales after both VAT reforms, with larger measured effects after the 2020 increase. However, the evidence suggests that these changes primarily reflect formalization of transactions, migration toward electronic payments, improved reporting compliance, and intertemporal expenditure timing rather than persistent growth in real demand. Post-reform trend coefficients indicate gradual normalization in subsequent months. ECM estimates suggest that approximately 56% of short-run disequilibrium is corrected within one month. Findings are robust across alternative specifications. The paper contributes new evidence from the Gulf region by showing that retail transaction indicators may overstate real consumption responses when tax reforms coincide with rapid financial digitalization. From a sustainability perspective, the findings highlight the role of digital financial systems and modern tax administration in improving economic transparency, strengthening fiscal sustainability, enhancing formal-sector integration, and supporting the institutional transformation objectives of Saudi Vision 2030. The results imply that fiscal-policy evaluations should jointly account for tax administration reforms and changes in payment technology. Full article

Stablecoins are increasingly viewed as interoperable settlement layers within electronic commerce, yet the mechanisms through which they become activated as payment instruments remain underexplored. Extending the Unified Theory of Acceptance and Use of Technology (UTAUT) with platform interoperability and regulatory perception, this study develops a sequential adoption-to-activation framework in which technological evaluation, ecosystem compatibility, and institutional legitimacy jointly shape adoption intention and behavioral activation likelihood (BAL). Survey data from 400 digitally experienced Korean consumers with prior experience in digital payments and/or crypto-asset–related transaction environments were analyzed using hierarchical regression with HC3 robust standard errors and bias-corrected bootstrapped mediation testing. The findings show that platform interoperability exerts the strongest influence on adoption intention and also has a significant direct effect on BAL, while social influence exhibits both direct and indirect effects. By contrast, facilitating conditions and regulatory perception operate primarily through adoption intention, and performance expectancy and effort expectancy show no significant direct or mediated effects once ecosystem-level and institutional determinants are incorporated. These results identify a boundary condition of UTAUT in ecosystem-integrated payment environments: in digitally mature multi-sided commerce ecosystems, ecosystem compatibility and institutional legitimacy can operate as structurally prior conditions of activation, suggesting that stablecoin payment activation is better understood as a sequential process shaped by platform-level coordination than as a conventional intention-to-use decision centered on marginal functional gains. Full article

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the evolving and adaptive strategies employed by fraudsters. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for detecting fraudulent activities in large-scale financial datasets. This paper presents a comprehensive survey of ML/DL approaches for financial fraud detection. The survey systematically reviews existing research across multiple methodological paradigms, including classical supervised learning, anomaly detection, graph-based methods, deep neural networks, multimodal architectures, and cost-sensitive learning frameworks. Particular emphasis is placed on emerging techniques such as graph neural networks, transformer-based architectures, and federated learning approaches designed to address privacy and scalability challenges. In addition to reviewing model architectures, this work analyzes key challenges inherent to fraud detection systems, including extreme class imbalance, concept drift, adversarial behavior, data privacy constraints, and real-time deployment requirements. Furthermore, the survey examines evaluation methodologies, highlighting the limitations of commonly used metrics and discussing more realistic evaluation strategies that incorporate operational costs and risk management considerations. This paper also provides a structured taxonomy of fraud detection methods, comparative analyses of commonly used datasets, and a synthesis of current research trends. Finally, open challenges and promising research directions are identified, including adaptive learning systems, interpretable Artificial Intelligence models, graph-based behavioral modeling, and privacy-preserving collaborative fraud detection frameworks. Full article

This study examines how the COVID-19 period is associated with changes in digital payment usage, rather than simply whether adoption increased, in Saudi Arabia using monthly data from January 2019 to July 2025. An Interrupted Time Series (ITS) approach is employed to assess both the immediate and long-term effects associated with the pandemic on a digital payment Intensity (DPI) index constructed from national point-of-sale (POS) transaction data to capture aggregate electronic payment usage relative to cash withdrawals. The results show that the onset of the COVID-19 period is associated with a sharp and statistically significant one-time increase of approximately 7 to 13% in digital payment intensity, followed by stabilization at a higher level rather than sustained acceleration. This finding challenges the common view that digital payment adoption followed a continuously accelerating path, instead showing that the pandemic induced a discrete upward shift without altering the underlying growth trajectory. The estimated effects remain robust across multiple model specifications, including dynamic ITS models, seasonal adjustments, alternative break dates, exclusion of overlapping usage variables, and parsimonious infrastructure-only models. Inflation and ATM usage consistently show negative associations with digital payment intensity, highlighting the role of macroeconomic stability and cash substitution in shaping payment behavior. The study therefore offers a more nuanced interpretation of post-pandemic digital adoption by showing that the main effect of COVID-19 was a one-time level shift rather than a lasting change in growth dynamics. Focusing on aggregate usage intensity rather than access or account ownership, it provides a system-level perspective on digital payment behavior in response to large-scale shocks. Overall, the evidence suggests that the pandemic period coincided with a discrete upward realignment in digital payment usage in Saudi Arabia, reflecting the interaction between crisis-driven behavioral change and strong pre-existing digital infrastructure under Vision 2030. Full article

National legislative frameworks governing prescribing, pricing, reimbursement, and dispensing play a decisive role in shaping access to medicines. This study examines the financial availability of medicines in Bulgaria and North Macedonia through a comparative review of national pharmaceutical legislation, pricing mechanisms, reimbursement models, and digitalisation policies, assessed in relation to European Union standards. The findings indicate that access to medicines in both countries is shaped by the combined effects of multiple regulatory and financial instruments rather than by individual policy measures. Both systems apply strict control of prescribing and dispensing, external reference pricing, and positive reimbursement lists, reflecting alignment with international recommendations. However, significant differences in policy design lead to divergent access outcomes. Bulgaria’s more advanced digitalisation of prescribing and reimbursement, including mandatory electronic prescribing for selected therapeutic groups, enhances regulatory oversight and expenditure control but is associated with higher patient out-of-pocket expenditure, partly due to the application of the standard value-added tax on medicines. In contrast, North Macedonia combines lower taxation with capped patient co-payments, higher regulated pharmacy margins, and fixed pharmacy remuneration per prescription, contributing to improved financial affordability for patients while supporting pharmacy sustainability. Additional instruments, such as the Generics without Co-Payment List, further strengthen patient financial protection. The study provides comparative evidence relevant to pharmaceutical policy reforms and highlights the importance of balanced regulatory approaches that promote affordability, system sustainability, and equitable access to medicines. Full article

This study analyses the transition from traditional barrier-based toll collection to a free-flow tolling (FFT) system on a national motorway corridor. The aim is to quantify how FFT affects mobility, safety and environmental performance when physical toll plazas are replaced by overhead gantries. Operational data at toll barriers and booths are first characterised in terms of traffic volumes, queue events and accident frequency, and a set of Key Performance Indicators is defined to describe both mobility and environmental effects. Travel times are modelled for light and heavy vehicles, distinguishing between electronic toll collection and manual payment, while demand variations are estimated using elasticities with respect to travel time. Environmental impacts are assessed through an energy-based model of deceleration, queueing and acceleration combined with fuel-specific emission factors for CO₂-equivalent and PM₁₀. The results show that removing physical toll plazas reduces queues by about 79.5% and is expected to reduce accidents in toll areas by roughly 50%, with CO₂-equivalent emissions at toll locations decreasing by up to 80% for light vehicles and 85% for heavy vehicles, and corridor-wide emissions also being significantly reduced, even when induced demand is considered. A final application to a photovoltaic green island on a decommissioned toll plaza illustrates how FFT can be coupled with infrastructure reuse to support cost-effective decarbonisation. Full article

This entry explains how China’s e-CNY, the retail form of its Central Bank Digital Currency, translates three design choices into improved access, affordability, and reliability: (1) enabling wallet-to-wallet payments on the CBDC ledger with settlement upon payment (SUP); (2) ensuring seamless integration at checkout with existing QR-code systems and popular payment apps; and (3) providing users with practical control through credentials stored on their devices and managed by licensed operators. With payment finality clarified in law and a two-tier structure in place, offline payments can shift to a hybrid architecture. It blends account- and token-based functionality across online and offline settings, incorporates tiered identity verification, and supports low-cost solutions. In essence, e-CNY demonstrates that strategic decisions regarding settlement, interoperability, and user control can expand financial inclusion while maintaining robust regulatory safeguards. Full article

The global movement towards a cashless society has prompted the payment of tax obligations through digital platforms and sources. In this international race to ensure that transaction payments are not hindered by the lack of physical cash, Nigeria is also making progress. Therefore, the focus of this study is to assess the implications of digital payment systems in enhancing the effectiveness of tax revenue collection in Nigeria. The analysis spans from the first quarter of 2009 to the fourth quarter of 2023, utilizing the Autoregressive Distributed Lag and Error Correction Model. The research uses the most active digital payment systems that have been in operation during the study period. These electronic payment types include digital cheques (CHQs), Automated Teller Machines (ATMs), Point-of-Sales (POSs), Mobile payment (MPY), and Web-based payment (WPY). These are the predictor variables, while the tax revenue collection (TXC) during this period is the dependent variable. The control variables include information and telecommunication technology penetration rate (ICTPR), inflation, and gross domestic product. The outcomes of this study reveal that, over the long term, a percentage change in CHQs, ATMs, MPY, and ICTPR is linked to a decline of 8.1%, 12.5%, 6.7%, and 22.4% in TXC, respectively. In contrast, WPY indicates a 7.2% positive increase in TXC while inflation exerts a positive increase of 46.7%. The Error Correction Model (ECM) suggests that the deviations from the long-term equilibrium in earlier years are being corrected at a rate of 3.9% in the current year. In the short term, it is noted that digital payment systems do not influence TXC. On the other hand, GDP maintains a significant negative influence on TXC, in both the long- and short-term. Given these results, the study recommends the establishment of a robust information and communication technology (ICT) infrastructure to enhance effective tax collection, even from rural areas and the informal sector. It is also important for the government to develop strategies that will bring the informal sector into the tax net. Full article

This study examines the interaction effects of green finance and digital platforms on China’s economic growth, employing panel data from 30 provinces spanning the period 2013–2023. Green finance is measured using green bonds and green credit, while digital platforms are proxied by electronic payment and e-commerce penetration. Previous empirical findings indicate that both green finance and digital platforms significantly contribute to economic growth. A 1% increase in the interaction term between green finance and digital platforms, based on fixed effects models with robust standard errors, results in a 0.0204% increase in GDP, supporting the hypothesis of a positive interaction. Control variables including money supply, fiscal expenditure, inflation rate, fixed asset investment, and industrial structure, are included to isolate the net effects of green finance and digital platforms on GDP growth, reinforcing the study’s econometric robustness. This study contributes novel evidence on how the integration of green finance and digital infrastructure fosters sustainable and inclusive economic development. Full article

In today’s digital economy, electronic payments are essential to supporting financial transactions. However, the danger of fraud also rises with company complexity and volume. This study uses machine learning and advanced analytics to investigate fraud detection in electronic payments. Using business tools like accounts, account types, and balance sheets, we spot patterns and trends connected to illicit activities. To detect and identify fraud, our study uses pre-existing data, machine learning algorithms, and infrastructure. The author has assessed the performance of several models, such as logistic regression, random forests, and k-nearest neighbor models, using criteria like accuracy, precision, and recall. To determine the most important characteristics for fraud detection, the author also conducts a significance analysis and examines the model’s interpretability. According to the current study’s findings, financial institutions and payment systems will be able to identify fraud more efficiently and gain an improved knowledge of the traits of commercial fraud. Full article

This study aims to map the scientific knowledge of bank profitability and its determinants. It identifies trends and gaps in existing research through a bibliometric analysis. To this end, 634 documents published in the Web of Science database over the last 54 years were analyzed using the bibliometric package. The results indicate an increase in the volume of publications following the 2008 financial crisis, focusing on analyzing the factors influencing bank profitability and economic growth. The Journal of Banking and Finance is the preeminent publication in this field. The literature reviewed shows that bank profitability depends on internal factors (size, credit risk, liquidity, efficiency, and management) and external factors (such as GDP, inflation, interest rates, and unemployment). In addition to the traditional determinants, the recent literature highlights the importance of innovation and technological factors such as digitalization, mobile banking, and electronic payments as relevant to bank profitability. ESG (environmental, social, and governance) and governance indicators, which are still emerging but have been extensively researched in companies, indicate a need for evidence in this area. This paper also provides relevant insights for the formulation of monetary policy and the strategic formulation of banks, helping managers and owners to improve bank performance. It also provides directions for future empirical studies and research collaborations in this field. Full article

This study investigates the convergence of blockchain, artificial intelligence (AI), near-field communication (NFC), and mobile technologies in electronic payment (e-payment) systems, proposing an innovative integrative framework to deconstruct the systemic innovations and transformative impacts driven by such technological synergy. Unlike prior research, which often focuses on single-technology adoption, this study uniquely adopts a cross-technology convergence perspective. To our knowledge, this is the first study to empirically map the multi-technology convergence landscape in e-payment using scientometric techniques. By employing bibliometric and thematic network analysis methods, the research maps the intellectual evolution and key research themes of technology convergence in e-payment systems. Findings reveal that while the integration of these technologies holds significant promise, improving transparency, scalability, and responsiveness, it also presents challenges, including interoperability barriers, privacy concerns, and regulatory complexity. Furthermore, this study highlights the potential for convergent technologies to unintentionally deepen the digital divide if not inclusively designed. The novelty of this study is threefold: (1) theoretical contribution—this study expands existing frameworks of technology adoption and digital governance by introducing an integrated perspective on cross-technology adoption and regulatory responsiveness; (2) practical relevance—it offers actionable, stakeholder-specific recommendations for policymakers, financial institutions, developers, and end-users; (3) methodological innovation—it leverages scientometric and topic modeling techniques to capture the macro-level trajectory of technology convergence, complementing traditional qualitative insights. In conclusion, this study advances the theoretical foundations of digital finance and provides forward-looking policy and managerial implications, paving the way for a more secure, inclusive, and innovation-driven digital payment ecosystem. Full article