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 Digital transformation has emerged as a critical driver of organizational competitiveness in today’s dynamic and technology-driven environment. While much of the discourse emphasizes the importance of advanced technologies such as artificial intelligence, cloud computing, and data analytics, the true enabler of sustainable transformation lies in human investment. This article explores the interconnected roles of education, culture of change, and human capital in ensuring the long-term success of digital transformation initiatives. Education represents the cornerstone of this process, as it equips individuals with digital literacy, technical competencies, and adaptive skills necessary to navigate continuous disruptions. Reskilling and upskilling initiatives not only address the skills gap but, also empower employees to innovate and contribute to organizational growth. Equally important is cultivating a culture of change within organizations. Transformation requires more than new tools it demands an organizational mindset that embraces innovation, agility, collaboration, and tolerance for experimentation.A supportive culture fosters engagement, motivation, and resilience among employees, ensuring that digital strategies are implemented effectively. Furthermore, the relationship between return on investment (ROI) and human capital highlights the need to rethink traditional performance measures. Rather than limiting ROI to short-term financial metrics, organizations should adopt human-centric approaches that evaluate productivity, innovation, employee satisfaction, and long-term adaptability. Human capital development thus becomes both a strategic investment and a source of sustainable competitive advantage. By integrating education, cultural adaptability, and human capital ROI into a unified strategy, organizations can move beyond surface-level technological adoption to achieve holistic and enduring digital transformation. This human-centered perspective underscores that while technology provides the tools, it is people who ultimately drive meaningful change.

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 The exponential growth of digital technologies and global connectivity has profoundly transformed the way personal, corporate, and governmental data are generated, transmitted, and stored. In this interconnected environment, cybersecurity has emerged as one of the most critical challenges facing both national authorities and international regulators. The increasing reliance on cloud computing, artificial intelligence, and data analytics has intensified the flow of information across borders, raising complex questions concerning data sovereignty, privacy protection, and jurisdictional authority. This paper examines the evolving landscape of cybersecurity laws and the regulation of cross-border data flows, with particular emphasis on the interplay between national interests, international norms, and global trade. It reviews major legal frameworks—including the European Union’s General Data Protection Regulation (GDPR), the U.S. CLOUD Act, and data governance models in China and emerging economies—and analyzes how these systems shape the transnational governance of digital information. The study also explores the tensions between privacy rights and state security imperatives, ethical implications of data localization, and the prospects for global harmonization of cybersecurity norms. By integrating legal analysis, policy comparison, and theoretical perspectives on digital sovereignty, this paper contributes to ongoing academic debates about how to secure cyberspace while preserving openness, innovation, and human rights.

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 Emerging technologies have become the cornerstone of entrepreneurial transformation and the driving force behind the development of innovative startups. As the digital economy expands, startups increasingly rely on advanced technological tools—such as artificial intelligence (AI), blockchain, Internet of Things (IoT), cloud computing, and big data analytics—to create competitive advantages, scale operations, and enhance market adaptability. This paper explores the critical role of emerging technologies in entrepreneurial business development and the management of innovation in startups. Drawing on theoretical frameworks in innovation management, digital transformation, and entrepreneurial ecosystems, the study investigates how new technologies empower startups to achieve efficiency, differentiation, and resilience in rapidly changing markets. It highlights both opportunities and challenges, including regulatory barriers, cybersecurity threats, resource limitations, and the ethical dimensions of technology adoption. Through a synthesis of global case studies, the article demonstrates how startups across diverse regions leverage technological advancements to disrupt industries, attract venture capital, and foster sustainable growth. The findings underscore the necessity of integrating innovation management strategies with emerging technologies to enhance entrepreneurial success, shape dynamic ecosystems, and ensure long-term value creation. Ultimately, this study contributes to academic debates and practical insights for entrepreneurs, policymakers, and investors aiming to harness technology for sustainable entrepreneurial growth.

Abstract In the contemporary digital landscape, libraries stand at a critical crossroads between tradition and innovation. Once perceived as physical repositories of books and manuscripts, libraries today are undergoing a profound transformation in their structure, services, and purpose. In the digital transformation era, libraries are increasingly adopting emerging technologies to enhance their information management processes. This paper explores the integration of Artificial Intelligence (AI), cloud computing, blockchain, the Internet of Things (IoT), and mobile technologies in modern library environments. These technologies have significantly improved the efficiency of cataloging, information retrieval, resource sharing, and user interaction. By implementing AI-driven search tools, cloud-based platforms for digital preservation, blockchain for secure transactions, and IoT for inventory management, libraries have evolved into intelligent information centers. The study is based on a review of recent literature and practical applications from global library systems. It also highlights key challenges such as implementation costs, data privacy concerns, and the digital divide among users. Overall, the paper emphasizes that strategic and ethical adoption of emerging technologies can transform libraries into inclusive and efficient hubs for knowledge dissemination and community engagement in the 21st century.