Advanced Journal of Management, Humanity and Social Science

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 Information and Communication Technology (ICT) has emerged as a cornerstone of economic and social progress in the 21st century, significantly influencing technological innovation. This study examines the direct and indirect effects of ICT on technological innovation (TI), with a focus on the mediating roles of financial development (FD) and human development (HDI) in 15 selected developing countries (China, India, Brazil, South Africa, Egypt, Nigeria, Indonesia, Malaysia, Iran, Argentina, Thailand, Kenya, Vietnam, Colombia, Pakistan) over the period 2000–2023. Using the panel autoregressive distributed lag (P-ARDL) model, both short- and long-term relationships among variables are analyzed. Results indicate that ICT has a positive and statistically significant impact on TI (coefficient = 0.39, p<0.01), with FD and HDI mediating 28% and 35% of this effect, respectively. Economic growth (GDPG) also positively influences TI. These findings contribute to endogenous growth theory by emphasizing the interplay of technology, finance, and human capital in fostering innovation. For policymakers in developing countries, the results advocate for integrated strategies that enhance ICT infrastructure, financial systems, and human capital to accelerate technological innovation. The study also provides practical recommendations, including investments in broadband, venture capital, and education, to support sustainable innovation-driven growth.

Abstract The escalating sophistication of financial fraud necessitates a paradigm shift from conventional detection systems toward frameworks characterized by heightened intelligence, security, and transparency. The present study addresses a critical lacuna in the extant literature by proposing a novel, synergistic architecture that integrates Deep Learning (DL) with Blockchain technology to manifest a robust ecosystem for fraud detection. A dual-core engine is introduced, comprising: (1) a Long Short-Term Memory (LSTM) network, optimized for the capture of temporal dependencies within transactional data, and (2) a permissioned Hyperledger Fabric blockchain, which serves as an immutable trust layer for data integrity and the automated execution of responses via Smart Contracts. The proposed model underwent rigorous evaluation utilizing the benchmark IEEE-CIS Fraud Detection dataset. The framework achieved an exceptional F1-Score of 0.98 and an AUC of 0.99, thereby significantly outperforming standalone DL models and traditional methodologies. It is demonstrated, crucially, that by ensuring data integrity, the blockchain layer enhances the model''s resilience against data poisoning attacks—a critical vulnerability in modern artificial intelligence systems. Performance analysis reveals a mean transaction latency of 450ms under significant load, confirming the system''s viability for real-time deployment. This research establishes a new benchmark for secure artificial intelligence in finance, providing evidence that the fusion of DL and blockchain can create a transparent, auditable, and highly accurate defense against sophisticated financial fraud, thereby paving the way for a new generation of trustworthy computational systems in critical sectors.

Abstract Traditional credit risk models, which rely primarily on static and historical financial records, are increasingly insufficient in addressing the complexities of modern economies. Their retrospective orientation often fails to capture the real-time operational health of borrowers, resulting in suboptimal lending decisions. This study proposes a novel smart framework that integrates high-frequency Internet of Things (IoT) data streams with Explainable Artificial Intelligence (XAI) methods to enable dynamic and transparent credit risk assessment. The architecture incorporates diverse real-time operational signals—including supply chain logistics, equipment condition, production volumes, and inventory status—to construct continuously updated borrower risk profiles. At its core, the framework combines a Graph Neural Network (GNN) to capture intricate interdependencies within supply chains with a Long Short-Term Memory (LSTM) network for temporal analysis of IoT sensor data. An additional XAI layer, implemented through SHapley Additive exPlanations (SHAP), ensures interpretability of model outputs, thereby supporting regulatory compliance and fostering stakeholder trust. To evaluate the framework, a hybrid dataset was constructed, combining traditional financial statements with simulated IoT streams that mimic realistic business operations. Experimental results highlight a substantial performance improvement over conventional approaches, achieving an Area Under the Curve (AUC) of 0.97. Moreover, the XAI module generated transparent, feature-based explanations for changes in risk scores, offering actionable insights for lenders. This research argues that the convergence of IoT and XAI signals a paradigm shift from static, retrospective risk models to proactive, dynamic, and interpretable credit risk management, enabling financial institutions to make better-informed and timely lending decisions.

Abstract The integration of agile methodologies into digital transformation initiatives within public sector organizations represents a critical yet challenging endeavor in contemporary governance. This article examines the paradoxical relationship between Agile principles—emphasizing flexibility, iterative delivery, and self-organizing teams—and the inherently bureaucratic nature of public administration, characterized by hierarchical structures, rigid procurement frameworks, and risk-averse cultures. Through a systematic synthesis of empirical studies published between 2014 and 2025, this research identifies four primary categories of implementation challenges: institutional and regulatory barriers, procurement and contractual misalignments, cultural and resistance factors, and resource and capability constraints. The findings reveal that while agile adoption can significantly enhance transparency, responsiveness, and citizen-centered service delivery, successful implementation requires fundamental adaptations rather than wholesale methodological transplantation. A multidimensional framework proposed, integrating legal-procedural adaptations, hybrid governance models, tailored procurement mechanisms, cultural transformation strategies, and iterative implementation roadmaps. This research contributes to both public administration theory and digital government practice by providing evidence-based guidance for navigating the inherent tensions between agility and accountability in democratic governance contexts.