Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Decoding U.S. Retail Dynamics: A Holistic Strategic Analysis Using PESTEL and Porter’s Five Forces Frameworks
No full textThe U.S. retail industry is undergoing rapid transformation driven by technological advancements, changing consumer behaviors, consumers preference & choice, regulatory shifts, and competitive pressures. It’s important for retailers to understand factors that may affect the business environment, its surroundings, socio-economic trends, competitiveness in the market for gaining strategic advantages. This article portrays a holistic approach analyzing the U.S. retail industry by integrating two recognized frameworks PESTEL analysis and Porter’s Five Forces. PESTEL analysis helps retailers to understand the macro-environmental factors like political, economic, social, technological, environmental and legal factors, whereas Porters’ Five Forces model enables retailers to assess industry-level competitiveness, competitive intensity, bargaining power, entry barrier, potential disruptions. This research paper is designed to serve as a valuable resource for retailers, investors, policymakers, and industry stakeholders seeking to navigate the evolving U.S. retail market dynamics and maintain competitive advantage. Here, we designed our plan to elaborate and explain the PESTEL analysis and Porter’s Five Forces frameworks and then analyze both models to the most relevant extent in the context of U.S. retail domain. In this paper we used relevant secondary (publicly available) data and information for analyzing macro-environment factors and assess U.S. retail industry in terms of Porter’s five forces. By synthesizing these perspectives, this study highlights and explains key insights into the challenges and opportunities shaping the retail sector in the United States of America. The findings include the increasing role of digitalization, regulatory compliance, shifting demographics, supply chain constraints, and sustainability trends in influencing strategic decision-making. Retailers can gain strategic advantages over their competitors by leveraging cutting-edge business intelligence tools, machine learning, and artificial intelligence tools in improving their business process.  
The Impact of Implementing the New Leasing Standards on Enterprises Financing Decisions
No full textThis study aims to investigate the impact of the new lease accounting standards CAS21 on enterprises\u27 financing decisions. This paper uses a multiple-time-point difference-in-difference (DID) model to conduct research. The findings indicate that changes in lease accounting standards are likely to change the financing decisions of enterprises and reduce their financing efficiency. The reduction in financing efficiency is mainly manifested in the increase in the aggressive debt behavior of enterprises. The reduction in enterprise financing efficiency varies significantly among leasing scales in different industries, enterprises of different ownership structures and different ages. The new lease standards reduce the financing efficiency by increasing financial leverage ratios. 
The Formation of the Public Agenda in the Brazilian Tax Reform: Difficulties, Coalitions, and Impacts
No full textThe text examines the formation of the public agenda on tax reform in Brazil, focusing on how its central narrative justified the proposed fiscal changes. The reform was presented as necessary for simplifying the tax system but faced resistance from regional and sectoral interests. Tax system distortions reinforced the need for reform. One key issue is the variation in tax rates across states, which increases transaction costs and limits market reach. Additionally, Brazil’s tax system is regressive, with consumption taxes placing a greater burden on lower-income groups. Tax increases have been linked to rising public expenditure, often justified by social spending, which may serve political purposes. This study adopts a qualitative approach, using document analysis and public policy frameworks. The reform incorporates regulatory, redistributive, and distributive policy elements, leading to disputes among groups with conflicting interests. The role of advocacy coalitions, involving actors with diverse or opposing interests, was central to shaping the process. The Executive, Legislative, and various interest groups advanced measures balancing fiscal priorities with political objectives. The findings indicate that tax reform was structured as a strategy to legitimise fiscal changes, with different actors seeking to maximise their gains. Achieving a more balanced tax system requires political coordination to align divergent interests. Without structural adjustments, the reform risks maintaining inefficiencies rather than addressing fiscal imbalances
The Role of Predictive Analytics in E-Commerce Conversion Rate Optimization
No full textThis paper explores the critical role of predictive analytics in optimizing conversion rates for e-commerce businesses. It examines various strategies, technologies, and methodologies used in predictive analytics, their implementation in e-commerce settings, and their impact on key performance indicators such as conversion rates, customer retention, and overall revenue.
Democratizing Software Development: The Impact of Cloud Infrastructure
No full textThe democratization of software development through cloud infrastructure has transformed the technological landscape by breaking down traditional barriers to entry. The shift from conventional data centers to cloud-based platforms has revolutionized how developers create, deploy, and manage applications worldwide. The emergence of containerization, particularly Kubernetes, alongside Infrastructure-as-Code practices, has established new paradigms in deployment automation and resource management. The advent of these technologies has particularly benefited emerging markets and smaller organizations, enabling them to compete effectively with larger enterprises while fostering innovation and economic growth in previously underserved regions. The integration of artificial intelligence and advanced automation tools further enhances accessibility and efficiency, creating opportunities for diverse participation in the global digital economy
The Role of Blockchain in Banking Fraud Detection: Enhancing Security and Transparency
No full textThe financial industry is becoming more concerned about banking fraud as cybercriminals use more advanced techniques to take advantage of weaknesses in conventional banking systems. This study investigates how blockchain technology can be used to identify and stop fraud in the banking industry. The decentralized and unchangeable characteristics of blockchain technology provide improved security, transparency, and fraud detection effectiveness. Banking organizations can develop strong fraud detection systems by combining blockchain technology with artificial intelligence (AI) and machine learning (ML). To examine the importance of blockchain as a new IT technology, this paper refers to "The Future of Banking Fraud Detection" and assesses the possible advantages, difficulties, and practical uses of blockchain in banking fraud detection
Middleware Messaging Archival Systems: A Comprehensive Analysis
No full textModern middleware messaging systems require sophisticated archival mechanisms to ensure reliable message storage and retrieval while maintaining optimal system performance. These archival systems implement advanced storage patterns, including write-ahead logging, distributed queue persistence, and intelligent indexing to support high-volume message processing. The architecture incorporates presence-aware routing strategies and store-and-forward mechanisms that guarantee message delivery in distributed environments. Performance optimization techniques such as message batching, parallel processing, and tiered storage enable efficient message handling across enterprise deployments. The integration of artificial intelligence enhances routing decisions and message transformation capabilities, while cloud-native features support flexible deployment models. These capabilities make middleware messaging archival systems essential for organizations requiring reliable message persistence, efficient retrieval, and sophisticated integration patterns
AI-Powered Cloud Migration: Transforming Enterprise Modernization Strategies
No full textThis article examines the strategic integration of artificial intelligence technologies within cloud migration and modernization initiatives across enterprise environments. By analyzing current implementation patterns and emerging frameworks, we demonstrate how AI-driven tools significantly enhance migration assessment, planning, and execution processes while reducing operational overhead and technical debt. The article explores how machine learning algorithms optimize application portfolio analysis, dependency mapping, and resource allocation prediction, enabling organizations to overcome traditional migration barriers. Furthermore, we investigate how automated containerization, infrastructure provisioning, and intelligent monitoring capabilities deliver measurable improvements in migration timelines and post-migration performance. Through multiple industry case studies, this article provides cloud architects and IT leaders with actionable insights on implementing AI-powered approaches to cloud transformation, addressing both technical and organizational considerations for successful modernization strategies. This article contributes to the growing body of knowledge at the intersection of artificial intelligence and cloud computing, offering a comprehensive framework for leveraging AI to drive efficient and resilient cloud adoption
Building Expertise in Data Engineering for AI Applications: A Comprehensive Guide
No full textData engineering has evolved significantly with the integration of artificial intelligence in the financial sector, demanding robust infrastructures and sophisticated practices. This comprehensive guide explores the essential competencies, tools, and best practices required for modern data engineers to excel in AI-driven financial systems. It details the transformation from traditional batch processing to real-time streaming architectures, examining distributed computing solutions, cloud-native implementations, and quality assurance frameworks. The guide addresses critical aspects of system architecture, security protocols, and compliance requirements while highlighting emerging trends in stream processing, edge computing, and automation technologies that shape the future of data engineering
Revolutionizing Energy Management: The Impact of AI and Machine Learning Technologies
No full textThe integration of Artificial Intelligence and Machine Learning technologies is revolutionizing the energy sector by transforming energy optimization, predictive maintenance, and smart grid management. From advanced demand forecasting to dynamic pricing mechanisms, these technologies enable sophisticated control and monitoring of power distribution networks. The implementation of predictive maintenance systems with sensor analytics and anomaly detection frameworks has enhanced equipment reliability and operational efficiency. Smart grid management through AI-driven optimization and edge computing capabilities has improved grid stability and monitoring capabilities. Technical considerations in infrastructure requirements and algorithm selection have led to optimized system performance, while emerging developments in quantum computing and privacy preservation technologies promise further advancements in energy management systems