The effectiveness of risk management within the Jordanian banks’ internal control systems and internal auditing is the focus of the study considering the moderate impact of AI in the form of expert systems and neural networks. This study aims to examine the impact of integrating AI in auditing and corporate governance in order to improve the organization’s ability to withstand adversity and endure over time. The study obtained data from 350 internal auditors from Jordanian conventional and Islamic banks through a structured survey. Using partial least square structural equation modeling (PLS-SEM), the study established a positive correlation between the effectiveness of internal auditing and the control of internal systems with risk management. Moreover, while neural networks have a weaker moderating impact, expert systems have a moderating impact on the relationship between the control internal systems and risk management. The study concludes that AI in the form of expert systems enhances the ability to recognize and eliminate risks through the development of internal control and audit functions. It also proposes that the study enhances the understanding of agency theory and the theory of technological superiority by demonstrating the role of AI in aiding human auditors to improve the governance systems in an organization. Moreover, the results assist bank managers, policymakers, and regulators to inform the integration of AI systems and tools to improve risk management practices.

This study examines the impact of corporate social responsibility (CSR) and internal control (IC) on corporate reputation (CR) in the context of digital transformation (DT), which serves as a moderating variable. Furthermore, a sample of 324 individuals was selected from the research community of accountants, auditors, and other specialists working in the Iraqi environment, and data were collected using a list of questionnaires analyzed based on a Likert scale. The stability and reliability of the scale were verified using Cronbach’s alpha and the split-half method according to Spearman-Brown’s value, and several statistical tests were used to verify data distribution and potential bias. In addition, regression models were designed to test four main hypotheses. The results showed a positive impact of both corporate social responsibility and internal control on corporate reputation. Digital transformation technologies (DTT) strengthens the relationship between CSR, IC, and CR. This study offers academic value as it empirically analyzes the impact of those variables in the light of DT as a moderating variable. It also provides management, regulators, and stakeholders with possible strategies in accordance with the business environment, and provides policy implications for promoting institutional development and ESG requirements.

Aceh Province is a critical case for freight and infrastructure studies due to its geographic isolation, post-disaster recovery context, and heavy dependence on roads for over 95% of commodity transport. Despite its rich agricultural output, limited multimodal infrastructure hampers efficient distribution. This study aims to (1) analyze the effect of road network connectivity on commodity transportation and regional development, and (2) develop a forecasting model to predict future commodity transportation needs in Aceh Province. The Structural Equation Modeling (SEM) was applied to analyze the relationships among Road Network Connectivity (RNC), Freight Transport (FT), and Regional Development (RD), using data from 400 respondents across 23 districts. The SEM results show all latent variables are interconnected. FT plays a strong mediating role, linking connectivity improvements to development benefits. The study also develops forecasting models for commodity generation and attraction based on population, expressed as $Y$ = 2.209 $X_1$ and $Y$ = 2.807 $X_1$. These models highlight population as a reliable predictor of freight demand and can be generalized to other regions with similar geographic and infrastructure constraints. This research introduces a novel SEM-based framework for freight analysis in Indonesia and offers policy insights for integrating road infrastructure planning with regional development strategies.

Pavement distress is a critical factor in road maintenance planning, directly influencing transportation safety, serviceability, and infrastructure costs. While traditional mechanistic and statistical models provide limited accuracy, they often fail to capture the nonlinear and multi-factorial nature of pavement deterioration. This study addresses this gap by proposing an integrated machine learning (ML) framework that incorporates real-time traffic and climatic variables for predicting pavement roughness. The framework draws on multiple open-source datasets, Long-Term Pavement Performance (LTPP), Federal Highway Administration (FHWA) traffic volumes, and National Oceanic and Atmospheric Administration (NOAA) climate records, to construct a multidimensional feature space. Four predictive algorithms were benchmarked: Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Ensemble-based models achieved superior predictive accuracy, with Random Forest attaining R$^2 \approx$ 0.89 and Root Mean Square Error (RMSE) $\approx$ 0.61, outperforming traditional regression baselines. The findings highlight that ensemble learning can more effectively capture non-linear dependencies between structural, traffic, and climatic factors than alternative approaches. Beyond technical performance, the study illustrates the potential of integrating continuously updated environmental and traffic data into pavement management systems, offering a pathway to more cost-efficient, reliable, and sustainable maintenance planning.

Selecting an optimal logistics service provider is a complex multi-criteria decision-making problem that directly affects a company’s competitiveness. This paper proposes a hybrid MCDM model that integrates the Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods. FUCOM was used to determine the weight coefficients of seven criteria, while MARCOS was applied to rank ten potential logistics providers in the market of Bosnia and Herzegovina. The case study was conducted for the company Hygiene Pro Team from Banja Luka. The results showed that provider P9 represents the most favorable solution, which was confirmed by an extensive sensitivity analysis that verified the stability of the model. The proposed FUCOM–MARCOS model provides a robust framework for strategic decision-making in logistics.

The rapid growth of global trade has heightened the importance of efficient container handling, environmentally responsible operations, and high-performing equipment selection in sustaining the competitiveness of modern supply chains. Container Freight Stations (CFS) serve as critical operational hubs where loading, unloading, inspection, and temporary storage activities are conducted, thereby requiring equipment capable of safely and efficiently handling heavy-tonnage cargo while aligning with green port transformation goals. Forklifts, which constitute one of the core equipment groups in CFS yards, differ significantly in terms of lifting capacity, power systems, maneuverability, hydraulic performance, ergonomics, and environmental impact, transforming forklift selection into a complex, multi-dimensional decision problem shaped by both technical and Environmental, Social, and Governance (ESG)-oriented considerations. Incorrect equipment choices may lead to operational downtime, energy inefficiency, equipment failures, and occupational safety risks, particularly in operations involving loads exceeding 25 tons. To address these challenges, this study proposes a hybrid decision-making framework that integrates expert-driven fuzzy assessments with sustainability-based evaluation using the FF-Hamacher-MEREC-ARLON methodology. In the first stage, expert weights and criterion importance values were calculated through the FF-MEREC approach, while alternative forklifts were ranked using the FF-ARLON method in the second stage. Two sensitivity analysis scenarios were applied: one by modifying the tradeoff ratio within ARLON and the other by sequentially removing each criterion. In both scenarios, the fourth alternative consistently emerged as the most suitable option. Furthermore, comparative analyses using eight established MCDM techniques; ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS; demonstrated complete agreement with the proposed model, confirming the fourth alternative as the top-ranked choice. The findings highlight the robustness, reliability, and sustainability alignment of the proposed framework for high-stakes heavy-duty equipment selection in port-based logistics operations.

Global research trends on Over-The-Top (OTT) media services were examined through a bibliometric and systematic review of 176 peer-reviewed documents published between 2010 and 2024, sourced from 117 journals and books. The analysis conducted using Bibliometrix, VOSviewer, and the PRISMA methodology identified a compound annual growth rate of approximately 34% in scientific output from 2010 to 2023, indicating rapid expansion in OTT research. The most prolific contributors by publication count are Chakraborty, Soren, and Sridevi. The leading countries in this field are India, South Korea, and the United States. Key research themes include platform dominance, consumer behaviour, and the impact of COVID-19 on OTT adoption, with “OTT” and “Netflix” as dominant topics in scholarly discussions. Although the data were sourced exclusively from Scopus, the findings offer a comprehensive overview of the evolution of themes, principal research networks, and prevailing trends in the OTT research domain.