The digital transformation of companies enables process improvement, innovation, efficiency, and the strengthening of competitive advantage in the pastry industry in the market. The present research aims to determine the relationship between digitization activities and competitive advantage in pastry companies in the city of Huancayo. The methodological development was carried out in accordance with the general scientific method, using the hypothetical-deductive method with a non-experimental and cross-sectional design. It was an explanatory study that sought to determine the two-dimensional relationships between variables. Data was collected from 172 companies operating in the medium-scale pastry sector in the city of Huancayo. The information collected was processed using the structural equation modeling (SEM) method. The results show a significant relationship between three dimensions and competitive advantage, with a correlation coefficient of 0.841 in the technological infrastructure dimension, 0.794 in the marketing dimension, and 0.871 in the digital management and processes dimension. The main conclusion of the research is that digitization, understood as a comprehensive process, drives efficiency, innovation, and differentiation. In Huancayo's bakeries, digital marketing and process management significantly strengthen competitive advantage and business sustainability.

Human resources (HR) of various groups (e.g., ethnic minority or majority) and their quality play a crucial role in developing and promoting economic and social progress in every region. However, current methods of quality assessment, e.g., surveys, have not provided data-driven insight for policymakers to design targeted interventions. Machine learning is one of the emerging technologies that could analyze complex datasets to support data insight for policymakers in sustainable economic development. This study proposes a framework to predict the quality of HR from ethnic minority community by using various machine learning techniques (K-nearest neighbors, multilayer perceptron, gradient boosting, and voting classifier). To achieve the best model, two techniques for feature selection (recursive feature elimination and extra trees) are employed. In the experiments, the ethnic minority HR data has been used to conduct. Experimental results show that the gradient boosting consistently outperformed other models across feature selection techniques (≥0.99). The findings from this study enhance prediction methods for HR and provide valuable insights for policymakers to develop effective policies for ethnic minority communities.

This study examines the impact of information technology infrastructure efficiency and top management support on the successful adoption of e-learning systems in private Jordanian universities. Based on the DeLone and McLean Information Systems Success Model, data were collected from 265 faculty members, administrative staff, and students using a structured questionnaire. Statistical analysis using multiple regression and structural equation modeling revealed that both infrastructure efficiency (β = 0.41) and top management support (β = 0.38) significantly positively affect e-learning system adoption success, collectively explaining 64% of the variance in success. The results confirm that successful e-learning adoption depends not only on technical aspects but also requires strategic and financial support from top leadership. The study provides an integrated framework for university decision-makers, recommending continuous investment in technological infrastructure while enhancing the leadership role of top management to ensure the sustainability of e-learning initiatives.

The main objective of this study is to evaluate how government education policies influence senior high school students' views on ease of use, perceived usefulness, attitudes toward use, and readiness to use web-based learning, particularly in collaborative learning scenarios in high schools. This study explores the influence of the Technology Acceptance Model (TAM) on the adoption of e-learning through web-based tools, focusing on high school students in Semarang, Indonesia. This study collected data through a survey using a 5-point Likert scale from 150 participants in Semarang, Indonesia. For data analysis, the study used Partial Least Squares Structural Equation Modeling (PLS-SEM) along with WarpPLS 5.0 software. The study findings revealed a significant relationship between ease of use, perceived usefulness, and attitudes toward use, all of which positively impact students' willingness to use web-based learning tools for collaborative learning. Furthermore, this study also highlighted areas where the traditional TAM model could be improved by incorporating factors specific to the e-learning environment. This study enhances theoretical understanding of the factors shaping the adoption of web-based tools in e-learning among secondary school students in Indonesia, with implications emphasizing the need to refine the TAM framework to better encompass the complexities of technology adoption in educational settings, in line with the government's policy to increase e-learning adoption in the context of the Independent Curriculum Policy.

Internet of Things (IoT) and Internet of Medical Things (IoMT) networks provide a massive amount of data. These types of data need a protection level, such as an intrusion detection framework. Deep learning models become a powerful tool for this purpose. Therefore, this work proposes an intrusion detection framework based on a deep learning technique which employs TabTransformer and self-attention mechanisms to imprison intricate dependencies among tabular features and detect abnormal attack behaviors. Precisely, each numerical feature is mapped into a learnable embedding vector and augmented with positional encodings to preserve feature identity and inter-feature relationships within the embedding space. The main task for the proposed model is to achieve binary classification tasks the model should classify the traffic data as either normal or abnormal. Furthermore, the model utilized a benchmark dataset such as the CICIoMT2024. Furthermore, this type of dataset faces issues, such as imbalance. So, the system integrates SMOTE-based data balancing, Stratified K-Fold Cross-Validation, and threshold optimization to ensure fairness and reproducibility to accomplish a binary classification task. As a consequence, experiments on the CICIoMT2024 dataset yield superior results, achieving a mean accuracy of 99.85. Through SHAP-based interpretability, key features influencing model predictions are identified, confirming the framework’s transparency, robustness, and suitability for real-world ARP intrusion detection.

This study sets out to analyze the impact of the application of artificial intelligence on the performance of digital marketing in banking institutions with an emphasis on the mediating role of cloud business intelligence systems. A sample of 441 administrative workers of 12 different commercial banks across the Kingdom of Jordan took part in the study. The study used an online questionnaire method to obtain the required information and the SmartPLS4 statistical program to analyze the direct and indirect relationships of the study variables. The results clearly showed that the application of AI significantly increased the performance of digital marketing by improving the targeting and optimization of campaigns. Additionally, the results clearly showed that the cloud business intelligence system provided a considerable mediating result that increased the linkage of AI applications and its effects on marketing performance. This study aims to benefit banking executives and decision-makers by showing the positive potential of the application of AI technologies with cloud business intelligence systems on the performance of marketing operations. This study will remain valuable due to its practical significance because it offers an experience on the effects of AI on the performance of the banking sector’s digital marketing operations within the developing markets of financial institutions.

Cyber-attacks become manifested as a series of behavioral patterns, but not as an event, and many current security regimes stay based upon a static detection and fixed trust implementation. Such incongruence restricts their capability to act in a dependable manner in fluctuating and unpredictable threat situations. The existing artificial intelligence-based cybersecurity products mainly focus on the detection performance. Due to this, such systems will still be vulnerable to false positives, erratic reactions, and degradation of performance over time. This paper proposes a behavior-sensitive cybersecurity model that brings together sequential behavioral modelling, risk-adaptive cryptography implementation, and integrity-guaranteed learning in an architecture with closed loops. The temporally structured patterns of activity are considered as behavioral risk, which allows making proportional, not binary, trust decisions. Cryptographic policies are adaptively changed based on the inferenced risk, whereas learning updates are explicitly secured to maintain the model reliability as time goes by. The experimental findings indicate that the proposed framework can obtain a detection accuracy of 96.7% and F 1-score of 96.0, as well as a false positive rate decreased to 3.1%. Moreover, the adaptive response latency is also decreased by a factor of about 20-30% relative to the representative baselines and also enhanced stability in response to adversarial noise. These results indicate behavior-based intelligence.

In this research, the influence of business intelligence (BI) systems on business project development and sustainability is explored, with specific attention given to the role of electronic leadership (e-leadership) as the mediating factor. As organizations continue to function in a rapidly digital world that is dominated by project work, BI capabilities play a crucial role in increasing the quality of decision-making, project work efficiency, and business sustainability. The different dimensions of BI, including data integration, BI capabilities, real-time reporting, among others, have all been found to influence business project development positively, with the aid of electronic leadership that enables the implementation of BI, which, consequently, develops business strategies for sustainability. With this study, a quantitative research design was adopted. An electronic questionnaire was administered among administrative staff and project management personnel of various organizations that function within technology-intensive environments. A total of 400 responses were collected for the study. Structural equation modeling (Smart PLS version 4) was adopted for the study Findings In conclusion, findings have identified that business intelligence exerts a constructive influence on the development and sustainability of business projects in terms of planning, resource, risk, and value creation. In addition, the findings confirm that e-leadership is a mediating variable that increases information communication, collaboration, and decision-making through BI, thereby ensuring effective BI usage within a business project setting. Moreover, the findings support that information systems, such as BI, along with effective e-leadership, serve as a catalyst to ensure the sustainability of business projects. In that regard, this study will make a valuable contribution to the development of business intelligence and e-leadership concepts within a business project sustainability framework. This study will prove to be a very valuable resource to project managers, leaders, and government administrators in terms of utilizing BI-related technological applications as well as electronic leadership practices to enhance business project success rates.

The purpose of the study is to examine the antecedents of project management performance in the manufacturing industry of Saudi Arabia. A quantitative method was used, and an online survey was conducted to collect data from 221 employees via the convenience sampling technique. Data analysis was performed using the structural equation modeling to assess the relationship between constructs. The study findings depict that antecedent such as leadership (β = 0.557), compensation (β = 0.433), work environment (β = 0.643) and employee engagement (β = 0.357) have a positive and significant influence on project management performance. The study demonstrates that manufacturing organizations need to build strategic leadership by providing monetary and non-monetary benefits and developing a culture for a sustainable organization through employee encouragement. The research gives valuable insights for organizations to achieve better project performance and organizational efficiency. Organizations should work on sustainable systems that ensure economic transformation in alignment with Vision 2030’s agenda for workforce development and innovation.

User-generated content in academic weblog platforms fosters collaborative learning and knowledge dissemination. However, traditional weblog systems often lack transparent and verifiable mechanisms for recognising contributors, which limits the credibility of acknowledgement processes and the ability to provide authors with immutable proof of their accomplishments. In this paper, SBTBlogCert, a hybrid distributed data management framework that integrates centralised and decentralised infrastructures to acknowledge academic weblogs' user-generated content contributors, is introduced. For traditional Web 2.0 blog content management, a Supabase and PostgreSQL database is used. For Web 3.0 technologies, such as blockchain networks, Soulbound Tokens (SBTs), and the InterPlanetary File System (IPFS), are used to make sure that contributions are recognized in a transparent and decentralised way. A proof-of-concept prototype is designed and developed to evaluate the effectiveness of the hybrid distributed data management solution. This included performance metrics for web and blockchain, such as transaction latency, gas consumption, and transaction fees across multi-chain deployments. The results contribute to ongoing research in data and network science, especially in improving hybrid architectures that combine Web 2.0 cloud databases with Web 3.0 decentralised storage and verification networks.