Information and Communication Technology (ICT) is currently developing rapidly along with its increasingly important role for both individuals and organizations. One form of ICT development is E-Commerce which serves as a comprehensive virtual marketplace. Based on macroeconomics context, the role of E-Commerce needs to be explored further, especially the relationship between Business to Consumer (B2C) E-Commerce and Gross Domestic Product (GDP), a topic that remains underexplored. The purpose of this study is to observe the nexus between B2C E-Commerce and GDP using two main classifications, namely: 1) economic status and 2) geographical continent. A panel-data regression analysis was conducted involving 117 countries from 2016 to 2020. The results showed that B2C E-Commerce had a positive and significant effect on GDP. In addition, the increase in e-commerce has been found to have the potential for nation growth in both developing and emerging economies. Notably, Africa and Asia-Oceania continents presented considerable opportunities to harness E-Commerce as a driver of national economic development. These findings provide important managerial and policy implications for governments and stakeholders in defining strategies to promote inclusive digital economic growth.

This study explores the impact of leadership style and budget allocation on the success of cybersecurity projects, with a particular emphasis on the mediating role of risk mitigation strategies within the healthcare sector. A total of 406 valid responses were obtained from IT professionals, cybersecurity officers, and project managers across healthcare institutions using a structured online questionnaire. Structural Equation Modeling (SEM) with SmartPLS was employed to analyze the data and test the proposed hypotheses. The results revealed that both leadership style and budget allocation exert significant positive effects on cybersecurity project success. Moreover, risk mitigation strategies were found to mediate the relationships between the independent variables and project success, underscoring their critical role in translating managerial and financial support into effective cybersecurity outcomes. All hypotheses were supported. The findings offer key theoretical contributions to cybersecurity and project management literature and present actionable insights for healthcare administrators seeking to strengthen their cybersecurity posture through strategic leadership and efficient resource allocation.

Mental stress is a growing global health concern, closely linked to psychological, behavioral, and physiological disorders. Accurate and early prediction of mental stress is crucial for timely interventions and improved health outcomes. Despite numerous studies leveraging machine learning (ML) techniques for stress classification, many have overlooked the integration of systematic feature selection and comprehensive model evaluation, limiting generalizability and interpretability. To address these gaps, this study proposes a robust ML-based framework that combines optimized feature selection methods - Recursive Feature Elimination (RFE), Extra Trees (ET), and Boruta - with various classification algorithms including Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting, and voting classifier. The models were evaluated using 10-fold cross-validation and ranked using the TOPSIS multi-criteria decision-making approach. The experimental results demonstrate high predictive performance across models (accuracy ≥ 0.98), with RF, DT, MLP, and Gradient Boosting achieving perfect accuracy (1.00). Among all configurations, the RF-Boruta model emerged as the most optimal (TOPSIS score: 0.914558). These findings highlight the effectiveness of combining systematic feature optimization with ML classification for accurate and interpretable stress prediction, offering valuable insights for data-driven mental health interventions.

In 2023, the United Arab Emirates (UAE) Digital Government Strategy 2025 required government entities and companies to participate in transforming the country into a smart nation. The first phase named “digital transformation” focuses on digitizing all operations. As such, accounting information systems (AISs)—which collect, organize, and report financial data—must evolve in alignment with this vision. This study explores how professionals in the UAE’s government-owned aviation industry view AIS adaptation to meet national digital transformation goals. Data were gathered through semi-structured interviews with 17 AIS experts, each with at least two years of experience in both AIS and digital transitions. The responses were then open-coded into themes centered around the objectives, benefits, challenges, and organizational impacts of AIS transformation. The findings reveal that a variety of new technologies are being used. For example, blockchain is being applied to supply chains to enhance partner traceability. AI is being used to analyze large data sets, automate repetitive tasks, and integrate non-financial data, such as for fair value assessments, to support IFRS compliance. AI is also helping to improve GDPR compliance by identifying data vulnerabilities and triggering automated safeguards. Cloud computing is also being adopted to reduce idle capacity and offer scalable flexibility. Nevertheless, some challenges were noted, such as limited employee competence and resistance to adopting new systems.

This study aims to examine the impact of the utilization of virtual reality technology on operational performance, with a focus on understanding the mediating role of contextual awareness. The research was conducted in Indonesia, specifically at the Mobile Brigade Corps (Brimob) of the Aceh Regional Police (Polda Aceh). A quantitative approach was used in this study, employing a questionnaire consisting of questions designed based on variable indicators that had been validated and tested for reliability in previous research. Data were collected from a sample of 292 personnel from Brimob Polda Aceh. Structural Equation Modeling (SEM) with Partial Least Squares (PLS) approach was used for data analysis, allowing for the testing of hypothesized relationships between variables. The results of the study show that the utilization of virtual reality technology has a significant positive impact on operational performance, and that contextual awareness acts as a mediating variable influencing the relationship between virtual reality technology utilization and operational performance. Based on these findings, it is concluded that the utilization of virtual reality technology can enhance operational performance through the mediation of contextual awareness among personnel. These findings provide valuable insights for the application of virtual reality technology in improving operational performance in law enforcement agencies and suggest the importance of integrating new technologies to enhance the effectiveness of operational tasks.

Autonomous ground robots are increasingly relied upon in dynamic environments where reliable navigation and context-aware interaction are essential. However, existing robotic control systems often rely on cloud-based reinforcement learning (RL) frameworks or static algorithms that fail to adapt in real-time to noisy, unpredictable scenarios. These models typically overlook the constraints of edge deployment and lack robust integration with human interaction modalities such as voice and semantic object awareness. To address these limitations, this work proposes a fully embedded, IoT-enabled ground robot powered by a reinforcement learning-based adaptive control framework. The system leverages Raspberry Pi 4B+ as its core computational unit, integrating MQTT-driven communication, multimodal interaction through speech and vision, and lightweight policy convergence for obstacle-aware navigation. A novel RL-based state-action pipeline is trained and deployed entirely on-device, ensuring real-time responsiveness without external computation. Experimental evaluations show that the proposed framework reduces navigation errors by 22% and improves interaction latency by 37% over traditional PID and A*-based systems. The RL model converges in under 2200 episodes, with stable reward curves and high reliability across variable acoustic and physical terrains. This study showcases how low-cost, edge-based robots can achieve high autonomy and situational awareness contributing to future advancements in resilient, self-adaptive robotic systems within smart and resource-constrained environments.

The purpose of the current study is to examine the potentials provided by big data analytics and cloud computing in supporting entrepreneurial ventures. Quantitative approach was adopted through utilizing a questionnaire that was self-administered by (333) operational managers within entrepreneurial ventures operating in Saudi Arabia. SPSS was employed to screen and analyze gathered primary data. Results of study indicated acceptance of study hypotheses and confirmed that big data analytics and cloud computing are able to open many potentials for entrepreneurial ventures through big data analytics and its high potentials of informed decision making, and cloud computing along with its scalability and flexibility. The study suggested that entrepreneurs and their teams must have the necessary skills and knowledge. Investing in education and training programs can help entrepreneurs and their teams to stay up-to-date with the latest technologies and best practices in these areas. Further recommendations were presented in the study. Examining the potentials provided by big data analytics and cloud computing in supporting entrepreneurial ventures is significant because it can help entrepreneurs to make informed decisions, enhance the customer experience, increase efficiency, gain a competitive advantage, and drive innovation.

The study is based on theoretical and empirical studies on the role of the internet in consumption and related factors. This paper synthesizes the channels through which the Internet influences consumption and analyzes the factors affecting this relationship. Methodology adopted for this research is an empirical approach. We estimate the model using panel data through Fixed Effect Model Estimations The panel data is collected annually for 63 provinces in Vietnam. The data is sourced from the Statistical Yearbook and VCCI. The results obtained revealed the positive impact of the internet on consumption. The results indicate that the impact of the internet becomes stronger with the Mobile Internet. The paper finds that the internet plays a more significant role in urban areas compared to rural ones. It also shows that effective local governance positively influences the relationship between internet and consumption. The paper offers the first evidence that local factors, including the macroeconomic, institutional and social variables, are determinants of consumption in the context of Internet development.

Accurate crime prediction is crucial for effective law enforcement and security, enabling proactive resource allocation and risk reduction. Criminal behavior is influenced by complex, diverse socio-economic factors, necessitating advanced models capable of extracting intricate patterns from large datasets. This research presents a methodological and applied comparison of four primary categories of time series forecasting models: Statistical Models (AutoARIMA), Machine Learning models (AutoLightGBM), Deep Learning models (N-HiTS), and Foundation Models (TimeGPT). The study’s innovation lies in (1) integrating these diverse categories in a single comparative framework tailored for security decision-makers, (2) explicitly applying cutting-edge AI, particularly Foundation Models (TimeGPT) with pre-training on vast, multi-domain time series, for crime prediction for the first time, and (3) demonstrating a comprehensive application using daily crime data from Chicago (2017–2019), with the final month serving as a challenging test set for assessing robustness against sudden fluctuations. Results indicate that Foundation (TimeGPT) and Deep Learning (N-HiTS) models outperform in accuracy, effectively capturing nonlinear relationships and complex seasonality. Statistical (ARIMA) and traditional ML (LightGBM) models offer greater interpretability and faster training but are less adept at handling unexpected surges. This comparative, automated approach offers a practical solution for security agencies seeking AI adoption without significant programming complexity. The research underscores time series modeling’s role in enhancing security operations and explores new avenues for AI-driven proactive crime prevention using big data.

The effectiveness and efficiency of a machine learning model can be improved by feature selection, especially for high-dimensional datasets such as in cybersecurity. The proposed approach utilizes an enhanced version of the Rainbow agent with a memory storage structure. The suggested approach is assessed using two benchmark datasets namely RT-IoT2022 which is targeted towards IoT network security and the Android Malware Detection dataset which is meant for mobile security. The specification of the reinforcement learning model has been trained for 20 epochs and it is progressively enhanced through feature subsets to enhance classification accuracy. The results show that the AUC scores continuously increase were the one for RT-IoT2022 achieves 0.91 and Android at 0.93. Three well-known classifiers XGBoost, Random Forest and multi-layer perceptron (MLP) are used to test the power of the selected features. The outcome evaluation on RT-IoT2022 dataset shows that Random Forest achieved maximum accuracy (99.48%), followed by XGBoost (99.16%), while MLP secured 94.04% accuracy. In the Android malware dataset, XGBoost model gave the best accuracy of 89.50%, followed closely by Random Forest with 87.00% and MLP with 86.50%. This clearly shows that reinforcement learning based feature selection enhances accuracy and reduces computation. The research emphasizes utilizing dynamic feature selection in any cyber security application. The future will experiment with incorporating deep reinforcement learning as well as hybrid selection.