Purpose: This study investigates the transformative role of digital governance in Somalia’s public sector, with a focus on how digital solutions can enhance transparency, operational efficiency, and citizen participation. Methodology: To achieve this, the study develops a novel hybrid framework known as the SyMAP-Tech (Systematic Mapping and Participatory Technology Assessment) Framework. This approach combines systematic literature mapping with participatory technology assessment, enabling a comprehensive evaluation of the readiness, applicability, and impact of digital tools within fragile governance systems. Stakeholder engagement was central to this process, with localized digital platforms and citizen feedback mechanisms used to generate real-time input. In total, 116 digital participation tools, adapted from international best practices, were reviewed and mapped to Somalia’s governance ecosystem. Findings: The results reveal significant potential for digital solutions to bridge gaps in public accountability and service delivery, particularly in urban areas where infrastructure is relatively more developed. The analysis also identifies clusters of tools that are particularly effective in promoting inclusive policymaking, ensuring transparent procurement, and strengthening citizen oversight. Furthermore, the study highlights the importance of localized strategies that integrate both policy analysis and direct stakeholder participation to build legitimacy and trust in governance systems. Unique Contribution to Theory, Practice, and Policy: The research makes several contributions. At the theoretical level, it introduces the SyMAP-Tech Framework as a replicable model for evaluating digital governance within fragile and post-conflict states. In practice, it offers Somali institutions actionable insights into how digital solutions can be harnessed to improve service delivery, strengthen accountability, and enhance citizen engagement. For policy, the study recommends the adoption of context-driven digital governance strategies that are tailored to fragile state-building, with particular emphasis on transparency, trust-building, and institutional resilience.

Globalization and increasing cultural diversity in higher education demand innovative teaching approaches that support the development of intercultural competence. This study develops and tests a process-oriented model examining how undergraduate engineering students’ perceptions of faculty development support and institutional support are associated with students’ cultural intelligence, intercultural competence, perceptions of interculturally informed teaching practices, intercultural student engagement, and student learning outcomes. Survey data were collected from 814 undergraduate engineering students in Indonesia and analyzed using Structural Equation Modeling (SEM). The findings indicate that students who perceived stronger faculty development support reported higher levels of cultural intelligence, which was positively associated with intercultural competence and greater perceived implementation of interculturally informed teaching practices. Students who perceived more interculturally informed teaching practices also reported higher intercultural student engagement, which was positively associated with higher self-reported student learning outcomes. These findings provide an integrated perspective on how undergraduate engineering students perceive the relationships among institutional support, faculty practices, intercultural development, and learning outcomes, offering practical insights for supporting more inclusive and globally responsive engineering education.

This work introduces an explained, risk-conscious, and secure-by-design autonomic architecture of large scale distributed computing systems defining dynamic cloud-edge environments. The main issue it is expected to fix is the shortage of transparency, uncertainty quantification, and combined cybersecurity of the traditional black-box AI-driven distributed systems that restricts trust, scalability, and resiliency. In order to address these limitations, the suggested methodology combines some structured data preprocessing (Z-score filtering, entropy and variance extraction), probabilistic risk modeling, explainable AI based on SHAP, and Kubernetes KEIO-enabled adaptive orchestration into a single layered framework. Experimental analysis on cloud anomaly data sets shows good predictive accuracy of 91.1 percent, recall of 86 percent as well as precision of 79 percent as represented in the confusion matrix of 3333 true negative, 1000 true positive, 261 false positive, and 160 false negative. Risk scores are centred on 0.0, with most of the risk scores falling within the range of - 0.3 to 0.3, which is stable with respect to calibration. System availability indicates 94% of uptime, Mean Time to Recovery (MTTR) of 3.45 and 48% of scalability efficiency, which all indicate high uptime and efficient fault management. SHAP analysis reveals that power consumption (0.28) and memory usage (0.27) had the largest contributions to risks. In general, the architecture is able to implement transparent, resilient and scalable autonomous decision-making which is appropriate to mission critical distributed infrastructures.

This paper evaluates the performance of AI-based software tools within intelligent decision-making systems, emphasizing their application in Industry 4.0 environments. Various AI techniques, including machine learning, deep learning, and natural language processing—are assessed across domains such as predictive maintenance, quality control, supply chain optimization, and energy management. To advance this field, we introduce a novel framework, RAISE-DM (Real-time Adaptive Intelligence Software Evaluation for Decision-Making), which combines real-time data acquisition from IoT devices with adaptive AI models for continuous decision optimization. Performance evaluation considers key parameters such as scalability, response time, accuracy, and interpretability. The study also highlights critical technical barriers like data heterogeneity and integration complexity, offering targeted strategies to address them. By providing a structured performance analysis and proposing a scalable evaluation model, this research contributes to the design of more efficient, transparent, and resilient AI-driven decision support systems applicable across industrial and cross-sector settings.

Purpose: This study aims to assess the factors influencing the intention of micro-entrepreneurs in Somalia to adopt mobile money services, using the Technology Acceptance Model (TAM) as the theoretical framework. Given Somalia’s lack of formal financial infrastructure since 1991, the research investigates how mobile money contributes to financial inclusion among micro, small, and medium-sized enterprises (MSMEs). Methodology: A quantitative research design was employed, with data collected from a sample of 140 respondents using a structured survey. The TAM was used to evaluate the influence of perceived usefulness, perceived ease of use, and behavioral intention on mobile money adoption. Data were analyzed using SPSS, and model adequacy was confirmed through statistical testing for parameter estimation. Findings: The study found that behavioral intentions, perceived usefulness, and perceived ease of use significantly influence the adoption of mobile money services among Somali MSMEs. Mobile money has a positive impact on financial inclusion, allowing businesses to engage more actively in the economic ecosystem. While the attitude toward financial inclusion shows positive trends, its direct statistical significance was found to be inconclusive. Unique Contribution to Theory, Practice, and Policy: This study extends the application of TAM in fragile-state contexts and contributes to the growing literature on technology-driven financial inclusion. It offers policy insights into the need for a robust regulatory framework to ensure the stability and security of mobile money systems. Practically, it encourages governments and financial institutions to expand mobile financial services to unbanked rural populations and urges service providers to focus on usability and functionality to drive customer adoption and retention.