The rapid uptake of AI in the financial industry has transformed how legal compliance, risk management, and decision-making are conducted. However, issues regarding the transparency, accountability, and legal compliance of emerging markets remain. This paper examines how data analytics mediates the relationship between the adoption of AI and legal compliance in the financial markets in Jordan. A quantitative research approach was employed, whereby data were gathered from financial institutions in Jordan and analyzed via SPSS and AMOS using Structural Equation Modelling (SEM). AI was found to have a significant and positive direct influence on legal compliance and a strong influence on data analytics capabilities. Additionally, data analytics was found to positively influence legal compliance and partially mediate the relationship between AI and legal compliance. This suggests that the outcomes of AI compliance are improved when additional analytics are employed that improve interpretability, transparency, and streamlining of the regulation. This study documents the empirical evidence from Jordan on the hitherto unexplored mediating role of data analytics in the AI-compliance literature. The findings have practical implications for financial institutions and regulators who wish to practice AI within the legal and regulatory constraints.

The rise of Bitcoin has revolutionized the financial landscape, but it has also opened the door to a new era of criminal activities. Criminals take advantage of the anonymity provided by Bitcoin to conduct illicit transactions and engage in fraudulent activities. To address this issue, this paper proposes a detection model using Graph Neural Networks (GNNs) to detect fraudulent activities in the complex financial systems of Bitcoin. From the GNNs, we use EvolveGCN and EvolveGGCN to compare between them and find a powerful model that can investigate the network construction of financial transactions and capture patterns and anomalies that traditional methods may miss. In the literature, there have been a limited number of studies on Bitcoin fraud detection using GNNs, especially EvolveGGCN. Therefore, in this paper, we focus on the detection of fraud in the Bitcoin network using EvolveGCN and EvolveGGCN. In addition, we used a more recent dataset called Elliptic++, which is an extension of the Elliptic Dataset. The dataset provides valuable information on the behavior and patterns of fraudulent actions in the Bitcoin network. The results show that EvolveGGCN outperforms other models in terms of precision, recall, F1 score, and micro-F1 score. With an F1-score of 0.90 and micro-F1 of 0.93 for detecting illicit transactions in the early time steps.

Digital transformation has a considerable impact on state institutions, forcing organizations and professionals to adapt to technological advancements. The objective of this study was to analyze the relationship between digital transformation and the job performance of employees at a hospital in the Junín region of Peru. The research is basic in nature and was developed using a quantitative approach with a correlational level. The population consisted of 552 administrative workers, and data was obtained from a sample of 227 employees who perform administrative activities within the hospital. Data analysis included descriptive statistics and the use of structural equation modeling with PLS -SEM estimation, employing SmartPLS 4.0 software. The results showed a positive and significant relationship between digital transformation and job performance. The dimensions of people, technology, processes, and quality showed significant effects on job performance, with statistically significant values (p < 0.05), confirming the robustness of the proposed structural model and its adequate fit. It is concluded that digital transformation significantly influences the job performance of administrative workers in the healthcare sector, contributing to process optimization, the strengthening of job skills, and improved organizational productivity. These findings highlight the importance of promoting comprehensive digital transformation strategies focused on human capital development and institutional efficiency.

This study examines the factors that influence platform economy development. A quantitative study was employed to assess the relationship between study constructs. Data were collected from 266 platform workers in Saudi Arabia. Structural equation modeling was used to perform statistical analysis to examine the relationships between study constructs. The results reveal that digital infrastructure, economic conditions, regulatory environment, digital skills, and platform service quality have a significant influence on platform economy development. Most importantly, digital skills play a major role to engage more workers on digital platforms to render services and earn more. Nonetheless, digital infrastructure and the regulatory environment also support building the confidence of workers while working on these platforms. Thus, the influencing factors explain 85 percent of the variance in platform development economy, which is above the satisfactory level. This indicates that a better infrastructure and skill set of workers play a major in contributing towards the platform economy. Thus, it is important for regulators to design better policies for platform operators and to open avenues for agencies that could play a major role in digital economic growth.

The vast usage and development of web technology generate numerous types of web pages. Besides, not all these types are legitimate webpages. Phishing sites mislead web page users into taking harmful actions. However, there is a need for a tool to address this type of problem. Deep learning models are used in dealing with web technology to detect whether the webpage is either legitimate or phishing. Herein, a novel Tabular Multi-Head Attention (TabMHA) model is presented to perform a binary classification task. The main task is to classify whether the webpages are phishing or not. The proposed model is trained and tested on a benchmark dataset related to phishing detection. It contains 5000 legitimate web pages and 5000 phishing ones; the overall is 10,000. Also, the feature numbers in the dataset are out of 48 features. As a consequence, the proposed model achieved a powerful performance compared with other models in the literature; the model achieved an accuracy level of 99.6%. This result is considered a promising result and can be integrated into real-world detection models.

This study investigates the impact of cash holdings and capital structure on firm valuation in Egypt's emerging market, examining how COVID-19 altered investor perceptions. The research employs explainable machine learning (ML) to uncover non-linear financial thresholds that traditional valuation models overlook. Egyptian listed firms from 2015 to 2022 are analyzed using a Super Learner ensemble (Extremely Randomized Trees, Extreme Gradient Boosting, and a Linear Regression meta-learner) alongside SHapley Additive exPlanations (SHAP) and partial dependence analysis, with the Super Learner's performance compared against conventional methods in assessing financial policy effects on Tobin's Q. Three key findings emerge: (1) Leverage exhibits a non-linear relationship with valuation, where extreme levels (LEV > 1.2) unexpectedly enhance firm value, challenging trade-off theory; (2) Cash holdings demonstrate threshold effects, with optimal value at ~40% of assets and sharply increasing marginal benefits beyond this point; and (3) COVID-19 amplified these dynamics, elevating the liquidity premium while penalizing excessive debt. The Super Learner significantly outperformed traditional statistical and ML models (R² = 0.572 vs. 0.19-0.47). Practical implications suggest that investors and managers in emerging markets should adopt dynamic cash-debt optimization to avoid undervaluation, while policymakers can use ML-driven thresholds to design crisis-responsive regulations. This study contributes to the literature by (1) identifying non-linear thresholds that extend trade-off and pecking order theories, (2) introducing explainable ML to valuation research to balance accuracy and interpretability, and (3) providing novel evidence of COVID-19's structural impact on investor behavior in emerging economies.

This study aims to develop and apply a Mixed Estimator Nonparametric Regression–Spline Truncated and Fourier Series (MENR–SF) to model the nonlinear relationships between behavioral factors and the prevalence of heart disease in Indonesia. The proposed approach simultaneously combines spline truncated estimators and Fourier series within a unified nonparametric regression framework, allowing each predictor variable to be modeled according to the specific characteristics of its relationship with the response variable. Parameter estimation is conducted using the Least Squares method, while the optimal number of spline knots and Fourier oscillations is determined based on the Generalized Cross-Validation (GCV) criterion. The application of the MENR–SF model to data from the 2023 Indonesian Health Survey (Survei Kesehatan Indonesia, SKI), with 38 provinces as the units of analysis, indicates that the best-performing model is obtained when the prevalence of daily smoking, the proportion of insufficient physical activity, and habitual consumption of fatty foods are modeled using spline truncateds, whereas the proportion of hypertension control is modeled using a Fourier series. The optimal combination, with three spline knots and three Fourier oscillations, yields a minimum GCV value of 1.197, low prediction error, and a coefficient of determination of 0.94, indicating an excellent ability of the model to explain variations in heart disease prevalence. These findings conclude that MENR–SF is a flexible and accurate approach for modeling complex nonlinear relationships in health data. The model offers enhanced flexibility and richer interpretability regarding the effects of behavioral factors, thereby holding strong potential to support data-driven health analysis and policy formulation.

The intelligent autonomous systems need to be reliable in the situations when there is uncertainty as well as nonlinear dynamics and time-varying disturbances. Traditional model-driven controllers are not flexible and purely learning-based models can be unstable and not easily interpretable. The current hybrid techniques strive to unite these paradigms, but they are generally based on offline optimization or loosely coupled structures of learning and control. This paper offers a hybrid soft computing and adaptive learning model based on combining fuzzy inference with an online learning process to make decisions in real-time. The fuzzy aspect provides the ability to deal with uncertainty and nonlinear mappings whereas the adaptive learning aspect optimizes control parameters through performance feedback with limited updates. Experimental analysis shows the presented framework can reach control accuracy of 95.2 which is 3-5 points better than the representative hybrid and learning-based baselines, with adaptation time lowered to 2.6 s. Stability analysis indicates a much lower level of control signal variance than with the unconstrained learning strategies. The primary value of the research is the single hybrid architecture that maintains the interpretability and allows further adaptation, which is a feasible and reliable solution to intelligent autonomous control in continuously evolving environments.

This research aims to evaluate the performance measures of cloud business intelligence (BI) systems facilitated by blockchain transparency, as well as to explore the role of real-time visibility as a mediator in Jordanian commercial banks. To accomplish its objectives, this research chose twelve commercial banks in Jordan, and it gathered its data from 356 participants belonging to the highest hierarchy levels, such as executives and senior management, divided into information technology, risk, and operations units. Partial least squares structural equation modeling (PLS-SEM) was adopted for the evaluation of the measurement model, as well as for the assessment of the postulated mediation hypotheses, mainly for its strengths in predictions and sophisticated models. There is evidence suggesting that the implemented transparency from blockchain technology increases the effectiveness of cloud business intelligence (BI), for which real-time visibility functions as an important determinant in this advancement and works as a partial mediator for its effects. These findings indicate that implementing blockchain technology aspects for cloud business intelligence (BI) improves data reliability, availability, and timeliness, which can empower financial organizations to develop more accurate and applicable final insights. The major contribution of this research study is in its representation of empirical work carried out in the financial setting for the economy of Jordan, which explicitly verifies the role of blockchain-based transparency for improving the performance capabilities of cloud business intelligence (BI) via real-time visibility.

The digital transformation era has profoundly altered how small and medium-sized enterprises (SMEs) compete, particularly those in traditional industries. This study examines the relationship between second-order digital marketing and purchase intention, emphasizing the mediating role of e-service quality in Ulos-based SMEs in North Sumatra, Indonesia. Digital marketing is conceptualized as a second-order construct composed of cost efficiency, incentive programs, interactivity, and site design, reflecting the multidimensional nature of digital strategies. Data were collected from customers of Ulos SMEs who had engaged with online platforms through a structured survey. The measurement and structural models were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirm that digital marketing significantly affects its four dimensions and positively predicts e-service quality. However, the direct effect of digital marketing on purchase intention was insignificant. Instead, e-service quality strongly affects purchase intention and mediates the relationship between digital marketing and purchase intention. The model explained 54.6% of the variance in e-service quality and 65.9% in purchase intention, indicating substantial explanatory power. The findings contribute theoretically by validating digital marketing as a second-order construct and clarifying the mediating role of e-service quality, thereby addressing prior inconsistencies in the literature. Practically, the results underscore that SMEs, particularly in cultural industries such as Ulos, must integrate service quality with digital marketing to convert exposure into purchase behavior. This study highlights how balancing cultural authenticity with digital innovation enables heritage-based SMEs to achieve sustainability and competitiveness in the digital economy.