Advanced Journal of Management, Humanity and Social Science

Abstract This study examines the impact of microfinance services on the financial sustainability and performance of medium-sized enterprises (SMEs) in Mazar-e-Sharif, Afghanistan, using data from 100 firms selected through systematic random sampling. The primary objective is to assess how microcredit access, savings services, and entrepreneurial development training affect return on assets (ROA) and financial sustainability (FS), while controlling for firm age and size. Data were collected via structured questionnaires administered to SME owners/managers, with reliability confirmed by Cronbach's alpha of 0.780 (N=100 items) and content validity established through review by 10 Afghan microfinance experts. Analysis employed descriptive statistics, Pearson correlation matrices, and multiple linear regression models following established SME research methodologies. The first regression model revealed that internal finance (β=0.012, t=10.78, p<0.001) and trade credit (β=0.008, t=7.23, p<0.001) significantly enhance ROA (R²=0.61, F=24.37, p<0.001), while non-institutional finance shows a negative effect (β=-0.130, p=0.017). The second model demonstrated strong positive impacts of microcredit (β=0.287, t=5.02, p<0.001), savings services (β=0.214, t=3.67, p=0.01), and entrepreneurial training (β=0.176, t=3.11, p=0.002) on financial sustainability (R²=0.65, Adj. R²=0.62, F=28.45, p<0.001), with larger/younger firms benefiting most. Nine of ten hypotheses were supported, underscoring microfinance's comprehensive role (financial + non-financial services) in fragile economies. Policy recommendations include government investment in energy infrastructure, transportation networks, and SME training programs to enhance competitiveness. These findings offer actionable insights for post-conflict development strategies.

Abstract The rapid integration of artificial intelligence (AI) systems into core societal institutions—from criminal justice and welfare administration to employment and border governance—has generated unprecedented challenges for international human rights law. This article examines three intersecting dimensions of the AI-human rights nexus: the contested question of AI legal personhood, the allocation of responsibility for AI-induced harms across complex value chains, and the evolving architecture of global AI governance. Drawing on the Council of Europe's Framework Convention on Artificial Intelligence (2024), the UN Guiding Principles on Business and Human Rights as applied to AI (2025), and emerging regulatory frameworks including the EU AI Act, this analysis argues that granting legal personhood to AI systems is neither necessary nor desirable for effective accountability. Instead, a functional approach that mandates human rights due diligence throughout the AI lifecycle, establishes accessible remedy mechanisms for affected individuals, and promotes regulatory coherence across jurisdictions offers a more promising pathway. The article synthesises findings from a doctrinal analysis of 45 international legal instruments, UN reports, and scholarly sources to propose a rights-based governance framework centred on mandatory human rights impact assessments, independent oversight, and meaningful stakeholder engagement with affected communities.

Abstract The aim of this study is to design and evaluate a hybrid model based on SEO metrics and explainable machine learning algorithms (XAI) to improve the ranking of digital advertisements. The research data consisted of 5,000 simulated records, which after preprocessing were analyzed using XGBoost, Random Forest, and Elastic Net models. Performance evaluation using metrics such as Accuracy, MAE, and RMSE indicated that the XGBoost model outperformed the others. To understand the model's decision-making logic, SHAP and LIME were employed. The results highlight the significant role of content quality, keyword relevance, and click-through rate in determining ad ranking. Additionally, fidelity and stability metrics of explanations showed that the best-performing model not only offers appropriate accuracy but also exhibits high transparency and stability in providing explanations. In the structural analysis, the mediating role of "user trust" in strengthening the effect of SEO metrics was confirmed. Overall, the findings suggest that combining SEO metrics with explainable models can lead to both improved prediction accuracy and enhanced transparency and trustworthiness in ad ranking.

Abstract This expanded article examines whether cognitive flexibility and emotional regulation predict post-traumatic growth (PTG) among adult survivors of natural disasters. Drawing on post-traumatic growth theory, the organismic valuing perspective, and the regulatory-flexibility framework, the manuscript argues that growth after disaster depends not only on the severity of exposure but also on survivors’ capacity to revise assumptions, tolerate emotional distress, and select regulation strategies that fit changing demands. A cross-sectional design is proposed with 328 adult survivors recruited 6 to 36 months after an earthquake, flood, landslide, storm, wildfire, or related natural hazard. The study uses the Posttraumatic Growth Inventory-Short Form, the Cognitive Flexibility Inventory, the Emotion Regulation Questionnaire, and the Difficulties in Emotion Regulation Scale, together with demographic, loss-severity, displacement, and social-support variables. Descriptive statistics, Pearson correlations, hierarchical regression, and assumption checks are specified in detail. The editable results show that cognitive flexibility and cognitive reappraisal are significant positive predictors of PTG, whereas expressive suppression and broader emotion-regulation difficulties are significant negative predictors after controlling for age, gender, education, injury, property loss, displacement, time since disaster, and perceived social support. The model explains a meaningful proportion of variance in PTG and highlights the need for integrated disaster mental-health interventions that combine cognitive flexibility training, reappraisal skills, emotional awareness, culturally sensitive expression, and community-based support. The manuscript is prepared in the AJMHSS style and includes APA in-text citations and an APA-formatted reference list. Statistical values, affiliation details, ORCID, and ethics information finalized with the author’s actual dataset before submission.

Abstract Effective communication is increasingly recognized as the critical determinant of success in complex project environments. Yet, despite decades of empirical research, communication, documentation, and knowledge management remain fragmented constructs within project management theory. This study proposes the Communicative Infrastructure Model CIM, a conceptual and mathematical framework that reconceptualizes project communication as a dynamic control system. The CIM integrates documentation usability D, knowledge integration K, risk communication efficiency R, and communication network quality CQ into a unified model based on systems theory, information entropy, and control stability analysis. Using a mathematical foundation derived from Shannon’s information theory and Lyapunov stability criteria, the model defines a Communicative Infrastructure Function CIF that quantifies how communication reduces uncertainty, mitigates risk, and stabilizes decision-making processes. The proposed framework positions communication not as a soft skill but as a measurable, strategic infrastructure that governs project equilibrium. By demonstrating that project systems achieve stability when communication efficiency exceeds a calculable threshold, this study establishes formal criteria linking communicative quality to project performance outcomes. A comparative analysis of recent studies (2020–2025) illustrates how existing empirical models of Agile collaboration, cross-functional coordination, and documentation practices can be mathematically integrated within the CIM. This theoretical advancement offers both researchers and practitioners a quantitative foundation for assessing and optimizing communicative effectiveness in project environments. It opens pathways for future empirical calibration through data-driven simulations, AI-enabled communication analytics, and real-time performance monitoring. The CIM thus redefines project communication as a cybernetic infrastructure of project management, capable of transforming risk, uncertainty, and knowledge flow into measurable system stability.