International Journal of Business & Computational Science
The International Journal of Business & Computational Sciences (IJB&CS) is a multidisciplinary, peer-reviewed, open-access journal that publishes high-quality research in the fields of business and computational sciences. Our journal is committed to ensuring the transparency, accessibility, and integrity of scholarly research.
PROMPT LINEAGE AND GOVERNANCE IN LLM-ENABLED DATA ENGINEERING: A REFERENCE ARCHITECTURE
modern data engineering ecosystems to automate data transformation, quality assurance, metadata generation, and analytical reasoning. While these models enhance productivity and adaptability, they introduce significant governance challenges due to their probabilistic behavior and heavy reliance on prompts as executable control artifacts. Unlike traditional data pipelines, where logic is encoded in version-controlled code, LLM-enabled systems often embed prompts in orchestration layers without formal lifecycle management, lineage tracking, or policy enforcement. This absence of prompt governance undermines reproducibility, auditability, and regulatory compliance in enterprise data platforms. This paper proposes a reference architecture for prompt lineage and governance in LLM-enabled data engineering environments. Drawing on principles from DataOps, MLOps, metadata management, and responsible AI, the architecture treats prompts as first-class governed assets. It enables versioning, lineage tracking, metadata capture, and policy enforcement across prompt creation, deployment, and execution. The proposed architecture integrates with modern lakehouse platforms, orchestration engines, and observability tools to provide end-to-end transparency across data, prompts, models, and outputs. This study contributes a structured and practical framework to support scalable, compliant, and trustworthy adoption of LLMs in data engineering workflows.
Copyright (c) 2024 Shambhu Adhikari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Large Language Models in Modern Data Engineering: A Systematic Review of Architectures, Use Cases, and Limitations
The rapid advancement of large language models (LLMs) since 2022 has significantly reshaped modern data engineering practices. Originally developed for natural language processing tasks, LLMs are increasingly integrated into data engineering workflows, including data ingestion, schema inference, metadata generation, transformation logic synthesis, data quality monitoring, and natural-language interaction with analytical systems. This systematic review examines the role of LLMs in contemporary data engineering, focusing on architectural integration patterns, practical use cases across the data lifecycle, and inherent limitations affecting reliability and governance. Following PRISMA-informed guidelines, peer-reviewed articles, preprints, and industrial reports published between 2022 and 2025 were analyzed. The review identifies Retrieval-Augmented Generation (RAG), hybrid vector-database architectures, and agent-based orchestration frameworks as dominant deployment strategies. Evidence suggests that LLM-assisted pipelines improve developer productivity, reduce manual coding overhead, and enhance accessibility of data platforms for non-technical stakeholders. However, persistent challenges remain, including hallucination, data privacy risks, limited explainability, operational costs, and scalability constraints. The findings emphasize the need for robust architectural safeguards, evaluation benchmarks, and governance frameworks to ensure safe and effective production adoption. This review contributes a structured taxonomy of LLM-centric data engineering architectures and outlines future research directions to support trustworthy, scalable, and auditable data platforms.
Copyright (c) 2025 Shambhu Adhikari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Application of Machine Learning for Optimizing Oil Well Production and Reservoir Management: A Simulation-Based Approach
Background: The oil and gas industry requires efficient reservoir management and accurate production forecasting to optimize operations and reduce costs. Traditional physics-based models, though reliable, are computationally intensive and require domain expertise. Machine learning (ML) offers a data-driven approach to predict production trends, optimize operational strategies, and enhance decision-making. This study evaluates various ML models, including regression, decision trees, gradient boosting machines (GBM), and deep learning, to determine their effectiveness in oil well production forecasting. Methods: A synthetic dataset simulating reservoir conditions, production histories, and operational parameters was used to train ML models. Linear regression, decision trees, random forests, GBM, and deep learning models were tested. Performance was measured using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Hyperparameter tuning and cross-validation were applied to improve model accuracy, and feature importance analysis was conducted to identify key factors influencing production. Results: GBM achieved the highest accuracy, with an RMSE of 3.5% and an MAE of 2.1%, outperforming other models in production forecasting. Deep learning models captured complex patterns but required high computational resources. Random forests showed strong generalization, making them effective for noisy datasets, while linear regression struggled with non-linearity. Overall, ML models improved forecasting accuracy and enabled real-time optimization of reservoir operations. Conclusion: ML models significantly enhance oil well production forecasting and reservoir management. GBM proved to be the most effective, balancing accuracy and efficiency. Integrating ML into oil well operations can reduce costs and improve decision-making. Future research should focus on real-world datasets and hybrid ML approaches to further refine predictive capabilities.
Copyright (c) 2024 Mohsin Saleem (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Systematic Review of Machine Learning Techniques for Predictive Maintenance In Industrial IOT (IIOT) Environments
Background and Purpose: Predictive Maintenance (PdM) has emerged as a critical component of smart manufacturing, driven by the proliferation of Industrial Internet of Things (IIoT) technologies that enable continuous monitoring of industrial assets. The extensive data generated through interconnected sensors and cyber-physical systems has created new opportunities for real-time equipment diagnostics, early fault detection, and improved operational reliability. Machine Learning (ML) techniques play a central role in transforming these heterogeneous data streams into meaningful insights, reducing unplanned downtime and enhancing productivity. Despite rapid advancements, significant challenges remain regarding model selection, performance evaluation, interpretability, and practical deployment in industrial environments. This study provides a comprehensive synthesis of ML techniques applied to PdM within IIoT ecosystems, examining methodological trends, strengths, limitations, and research gaps. Methods: A systematic review methodology was adopted following PRISMA 2020 guidelines. Peer-reviewed studies published between 2015 and 2025 were retrieved from IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. Boolean search strategies were used to identify literature focused on ML-based PdM models applied to IIoT data, cyber-physical systems, sensor networks, and digital twins. Data extracted from eligible studies included ML algorithms, datasets, feature engineering approaches, performance metrics, deployment frameworks, and identified limitations. Comparative and thematic analyses were employed to categorize methods and evaluate their effectiveness across different industrial contexts. Findings: Sixty-two studies met the inclusion criteria. The findings show that Deep Learning (DL) architectures, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and autoencoders, predominate in contemporary PdM research due to their capacity to learn complex temporal and multidimensional sensor patterns. Hybrid models integrating DL with signal processing and classical ML methods demonstrated improved robustness and predictive accuracy. However, the review reveals persistent challenges, including the reliance on controlled or semi-synthetic datasets, limited real-time validation, data imbalance, lack of model interpretability, and constraints in integrating ML solutions with industrial hardware. These limitations hinder the scalability and practical adoption of PdM systems in real-world manufacturing environments. Theoretical Contributions: The review synthesizes key theoretical perspectives underpinning ML-driven PdM. Data-driven modeling theory underscores the importance of high-quality sensor data and feature representations for accurate prediction. Systems theory highlights the interconnected nature of IIoT architectures and the need for interoperability across devices and platforms. Decision-support theory contextualizes the role of predictive analytics in optimizing maintenance planning and operational strategies. Additionally, emerging paradigms such as physics-informed ML and edge intelligence illustrate how theoretical advancements can bridge gaps between algorithmic accuracy and industrial applicability. Conclusion and Implications: ML-enabled PdM offers substantial potential to transform industrial asset management within IIoT environments. To achieve large-scale implementation, future efforts must prioritize data quality improvement, real-time processing capabilities, algorithm explainability, and seamless integration with edge and cloud infrastructures. Research should advance toward federated learning, transfer learning, standardized benchmark datasets, and hybrid physics-data models to enhance model generalizability and industrial adoption. A holistic, technically informed, and context-specific framework is essential for maximizing the impact of ML-driven PdM in smart manufacturing ecosystems.
Copyright (c) 2025 Mahmudul Islam (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Transforming Private Equity Operations Through Ai-Driven Workflow Automation: A Comprehensive Review
Introduction: The private equity (PE) industry is undergoing a rapid transformation driven by technological advancements, particularly artificial intelligence (AI) and workflow automation. Historically dependent on manual data processing, human judgment, and fragmented decision systems, PE firms now face pressure to enhance efficiency, transparency, and scalability. The integration of AI-driven automation tools is revolutionizing deal sourcing, due diligence, portfolio monitoring, risk assessment, and operational management. This review explores how AI-driven workflow automation is reshaping private equity operations and its implications for competitiveness, value creation, and decision-making. Methods: A comprehensive literature review was conducted using databases including Scopus, Web of Science, and ScienceDirect. The search focused on publications from 2015 to 2025 employing combinations of keywords such as “private equity,” “AI automation,” “workflow transformation,” and “digitalization in investment management.” Both peer-reviewed articles and industry reports were included to capture academic and practical insights. The studies were screened for relevance, methodological rigor, and applicability to PE operations. Data were synthesized thematically across five operational domains: deal sourcing, due diligence, portfolio management, compliance, and exit strategies. Results: The review reveals that AI-driven workflow automation significantly enhances operational agility, data accuracy, and strategic foresight in PE firms. Automated systems employing natural language processing (NLP), predictive analytics, and robotic process automation (RPA) streamline repetitive tasks, improve risk modeling, and accelerate decision-making. Firms integrating AI-based deal sourcing tools reported up to 30 - 40% faster transaction screening, while automated due diligence platforms reduced manual workload by 50–60%. Moreover, AI-based portfolio monitoring systems improved real-time visibility and performance tracking, contributing to better fund governance and investor relations. Conclusion: AI-driven workflow automation is transforming the private equity ecosystem from intuition-based management to data-driven decision-making. While implementation challenges such as high costs, data privacy, and talent shortages persist, the long-term benefits include greater operational efficiency, improved return on investment, and sustainable competitive advantage. Future success will depend on how effectively PE firms align AI strategies with governance frameworks and human expertise
Copyright (c) 2025 Wafaa Chmimo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Edge AI for Energy-Efficient Computing: A Systematic Review of Strategies, Challenges, and Future Directions
Background and Purpose: Edge Artificial Intelligence (AI) has emerged as a crucial solution for minimizing power consumption during real-time data processing in computing devices. Given the increasing demand for energy-efficient AI systems, this study systematically reviews Edge AI methods focused on optimizing energy efficiency. The research highlights existing challenges and explores future research directions to enhance Edge AI capabilities. Methods: This study employs a systematic review methodology, analyzing recent advancements in Edge AI, including new hardware systems, model optimization techniques, and software development tools. The review considers the interplay between model complexity, hardware constraints, security concerns, and the trade-off between performance and energy efficiency. Findings: The analysis reveals that optimizing Edge AI requires a multifaceted approach involving hardware innovations, lightweight models, and adaptive algorithms. Key challenges include balancing computational power with energy constraints, ensuring data security in edge environments, and maintaining real-time processing capabilities. Theoretical Contributions: This research identifies three significant research avenues: (1) integrating neuromorphic computing to enhance efficiency and mimic biological neural processes, (2) leveraging federated learning to improve privacy-preserving model training across distributed edge devices, and (3) developing adaptive AI architectures that dynamically adjust computational resources based on workload demands. Conclusions and Policy Implementations: To advance Edge AI, policymakers and industry leaders must prioritize the development of energy-efficient hardware, encourage research into low-power AI models, and establish regulatory frameworks for secure edge computing. Future work should explore interdisciplinary collaborations to further optimize Edge AI performance.
Copyright (c) 2025 Ndansi Seraphin Sigala (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Role of Project Management from Idea to Execution in Entrepreneurial Product Development
Background and Purpose: Entrepreneurial product development is a complex, high-risk process that requires structured management to transform innovative ideas into market-ready products. Startups often operate under conditions of uncertainty, resource constraints, and rapid technological shifts, making project management a critical factor for success. This research examines the role of project management methodologies, including Agile, Lean Startup, and Design Thinking, in supporting entrepreneurs from idea generation to commercialization. By integrating structured frameworks with adaptive, iterative processes, this study aims to identify best practices that enhance product viability, market responsiveness, and operational efficiency in entrepreneurial ventures. Methods: A qualitative research design was employed, incorporating case study analyses and semi-structured interviews with startup founders, product managers, and project leaders. The study investigated project management practices across multiple industries, including technology, healthcare, and consumer products. Thematic analysis was used to identify recurring patterns in project execution, decision-making processes, and the effectiveness of various project management methodologies. Comparative assessments of successful and failed startups provided insights into critical factors influencing product development outcomes. Findings: Project management plays a fundamental role in mitigating risks, optimizing resource allocation, and ensuring structured product development in entrepreneurial ventures. The study finds that Agile methodologies, particularly Scrum and Kanban, enable startups to maintain flexibility through iterative cycles, allowing continuous user feedback integration. Lean Startup principles, centered around the Build-Measure-Learn framework, support rapid prototyping and market validation, reducing wasted resources. Design Thinking complements these approaches by emphasizing user-centric innovation and problem-solving. However, startups face challenges such as resource limitations, time constraints, team coordination issues, and evolving market demands. The research highlights the importance of prioritization frameworks, such as MoSCoW analysis, to focus on essential product features while preventing scope creep. Effective risk management strategies, continuous performance monitoring through Key Performance Indicators (KPIs), and structured communication channels significantly enhance project execution. Case study analyses illustrate that startups that embrace structured project management approaches outperform those that rely solely on ad hoc decision-making. Theoretical Contributions: This study bridges the gap between traditional project management methodologies and the dynamic requirements of entrepreneurial product development. By integrating Institutional Theory, Resource-Based View (RBV), and Lean Thinking, the research demonstrates how structured project management enhances innovation, strategic alignment, and sustainable growth in startups. The findings contribute to the evolving discourse on how project management practices should be tailored to support entrepreneurial agility while maintaining structured execution. Conclusion and Policy Implications: Entrepreneurs benefit from structured project management approaches that combine strategic planning with adaptability. Governments, incubators, and investors should support startup ecosystems by promoting project management education, providing funding for structured development methodologies, and fostering mentorship programs. Future research should explore the integration of AI-driven project management tools to further enhance decision-making, risk assessment, and real-time project tracking in entrepreneurial environments.
Copyright (c) 2025 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
The Effects of Government Policies On SME Growth in Developing Economies: A Comprehensive Review
Background and Purpose: Small and Medium Enterprises (SMEs) are fundamental to economic development in developing economies, contributing significantly to employment generation, poverty reduction, and GDP growth. Despite their potential, SMEs face numerous constraints, including limited financial access, regulatory burdens, inadequate infrastructure, and challenges in trade integration. This study provides a comprehensive review of the effects of government policies on SME growth, assessing the effectiveness of various policy interventions while identifying the key challenges and sectoral variations that influence their impact. Methods: A systematic literature review methodology was adopted, focusing on scholarly articles, policy reports, and empirical case studies from 2005 to 2025. The study utilized electronic databases such as Scopus, Web of Science, JSTOR, and Google Scholar. Boolean search techniques were applied to retrieve relevant literature, and thematic analysis was used to categorize the findings into distinct policy areas. The review applied comparative analysis to examine policy effectiveness across different regions and economic contexts. Inclusion criteria were set to ensure relevance, prioritizing studies that explicitly examined the intersection of government policies and SME development within developing economies. Findings: Government policies have played a critical role in shaping the SME landscape through financial inclusion initiatives, regulatory reforms, infrastructure development, trade and export promotion, digitalization policies, and sustainability measures. Financial policies, including credit guarantee schemes, microfinance programs, and interest rate subsidies, have improved access to capital. However, challenges such as high collateral requirements, inefficiencies in fund allocation, and the exclusion of informal enterprises persist. Regulatory reforms aimed at simplifying business registration and tax compliance have facilitated SME formalization, yet bureaucratic inefficiencies and corruption continue to undermine their effectiveness. Investments in physical and digital infrastructure have enhanced SME operational capabilities, but regional disparities remain, particularly between urban and rural enterprises. Trade promotion policies have provided SMEs with access to international markets through export processing zones and trade facilitation programs; however, complex certification requirements and non-tariff barriers hinder participation. Digitalization policies have encouraged SME adoption of e-commerce and financial technology solutions, yet cybersecurity risks and digital skill gaps present ongoing challenges. Sustainability policies, including incentives for green technologies and eco-friendly financing, are gaining traction, though high implementation costs and limited financial support restrict their widespread adoption. Theoretical Contributions: The study integrates multiple theoretical perspectives to analyze the effectiveness of government policies in SME development. Institutional theory highlights the role of regulatory stability and governance quality in shaping SME growth. The resource-based view (RBV) emphasizes the importance of financial and technological resource availability in determining SME competitiveness. Entrepreneurship theory illustrates the role of government incentives in fostering a conducive business environment. Market failure theory justifies state intervention in addressing financing gaps and structural barriers, while public choice theory explores the influence of political and interest-group dynamics on SME policymaking. These theoretical frameworks collectively provide a multidimensional understanding of how government policies impact SME growth and sustainability. Conclusion and Policy Implications: The findings suggest that SME growth in developing economies requires a multifaceted policy approach that integrates financial accessibility, regulatory efficiency, infrastructure investment, and trade facilitation. Governments should enhance financial inclusion through alternative credit mechanisms, reduce regulatory bottlenecks to encourage SME formalization, and strengthen institutional capacity to combat corruption. Digital and physical infrastructure investments must be expanded to bridge regional disparities, particularly in underserved rural areas. Trade facilitation policies should address certification and logistical challenges to improve SME integration into global value chains. Sustainability policies should be reinforced with financial incentives and technical support to enable SMEs to transition toward environmentally sustainable practices. A holistic and sector-specific policy framework, aligned with regional economic contexts, is essential for maximizing SME contributions to economic growth and development.
Predictive Analytics and Its Role in Enhancing Consumer Experience in E-Commerce
In today’s digital world, where e-commerce platforms have become an integral part of the consumer landscape, businesses are constantly seeking innovative ways to differentiate themselves in an intensely competitive market. One powerful tool in this quest is predictive analytics. By leveraging data and sophisticated algorithms, predictive analytics enables e-commerce businesses to forecast consumer behavior, personalize shopping experiences, optimize inventory management, and ultimately enhance overall customer satisfaction. This editorial explores the role of predictive analytics in transforming the consumer experience in e-commerce and its implications for the industry. What is Predictive Analytics? Predictive analytics involves using historical data, statistical algorithms, machine learning models, and artificial intelligence (AI) to predict future outcomes based on past patterns. By identifying trends, behaviors, and preferences, businesses can forecast what will happen in the future, allowing them to make more informed decisions. In the context of e-commerce, predictive analytics is used to understand customer behavior, forecast demand, and enhance personalization in a way that elevates the overall consumer experience (1,2). Personalization: Tailoring the Shopping Experience One of the most significant ways predictive analytics enhances the consumer experience in e-commerce is through personalization. Today’s consumers expect personalized experiences that cater to their individual needs and preferences. A one-size-fits-all approach no longer suffices, and e-commerce businesses must use data-driven insights to create more customized experiences. Predictive analytics helps businesses tailor product recommendations to individual customers by analyzing previous purchase history, browsing behavior, and preferences (3,4). For example, Amazon’s recommendation engine is a prime example of predictive analytics at work. The platform analyzes user behavior, such as past purchases and searches, to recommend products that the customer is likely to be interested in (1,5). This personalized approach not only makes shopping more convenient for the customer but also increases the likelihood of conversion and repeat business. Additionally, predictive analytics can optimize marketing campaigns by ensuring that the right message reaches the right audience at the right time. By analyzing customer segments and their behavior, businesses can predict which offers or promotions will resonate most with specific groups (6). For instance, a customer who frequently purchases skincare products may receive targeted promotions for related items such as moisturizers or face masks. This level of targeted marketing improves the overall consumer experience by delivering relevant content and products directly to the consumer (7). Anticipating Customer Needs and Enhancing Customer Service Predictive analytics goes beyond just recommending products; it also plays a crucial role in anticipating customer needs and enhancing customer service. By analyzing data from customer interactions, e-commerce platforms can predict when a customer might need assistance, enabling businesses to offer proactive support (2,8). For example, predictive models can forecast when a customer might encounter an issue with their order—such as delays in shipping or inventory shortages—and preemptively offer solutions. This proactive approach ensures that customers feel valued and heard, reducing frustration and improving overall satisfaction (7). Chatbots powered by AI can also use predictive analytics to identify the type of assistance a customer needs, providing instant and relevant support without the need for human intervention (1,3). Moreover, predictive analytics helps businesses optimize customer service operations by identifying common issues and pain points. For example, if a significant number of customers have issues with a particular product or service, predictive models can flag these trends, enabling businesses to address them promptly (2,8). This data-driven approach leads to a more seamless and efficient customer experience, helping businesses resolve issues before they escalate. Optimizing Inventory Management and Delivery A critical aspect of e-commerce is the timely delivery of products. Predictive analytics plays a pivotal role in optimizing inventory management and ensuring that products are available when customers need them. By analyzing consumer demand patterns, e-commerce businesses can predict which products will be in high demand during certain periods, such as during holidays or promotional events (9). This predictive insight allows businesses to ensure that their inventory levels are aligned with demand, reducing the chances of stockouts or overstocking (7). For example, if an e-commerce platform predicts an increase in demand for a particular product, it can increase stock levels in anticipation, ensuring that customers can make purchases without delays. On the flip side, predictive analytics can also help businesses avoid the costs associated with excess inventory by forecasting slow-moving products and optimizing inventory turnover (9). Additionally, predictive analytics can improve the delivery process by predicting the best delivery routes and timeframes. With the help of AI and machine learning, e-commerce businesses can forecast demand in specific regions and adjust their logistics operations accordingly (10). This ensures faster and more efficient delivery, which is a key driver of consumer satisfaction. In fact, fast and reliable delivery is often cited as one of the top factors influencing consumers’ purchasing decisions (7). Enhancing Pricing Strategies Pricing is another area where predictive analytics can significantly enhance the consumer experience. Dynamic pricing, powered by predictive models, enables e-commerce businesses to adjust prices in real time based on demand, competition, and other external factors 6. For example, an e-commerce platform might lower the price of a product when demand is low or raise the price when demand is high. This creates a sense of urgency and encourages consumers to make a purchase at the right moment (1,5). Furthermore, predictive analytics can help businesses determine the optimal pricing strategy for different customer segments. By analyzing data on customer demographics, purchasing power, and preferences, businesses can create personalized pricing strategies that appeal to different types of consumers (1,6). The Impact on Consumer Trust and Loyalty Ultimately, the use of predictive analytics in e-commerce can significantly enhance consumer trust and loyalty. When customers are offered personalized recommendations, proactive customer service, fast deliveries, and fair prices, they are more likely to trust the brand and return for future purchases (7,6). Predictive analytics helps businesses build long-term relationships with customers by offering them the right products at the right time, creating a seamless and enjoyable shopping experience. In addition, when customers feel that their needs are anticipated and met, they are more likely to recommend the brand to others (7). Positive word-of-mouth and customer referrals are powerful tools in driving business growth, and predictive analytics contributes to this by fostering a deeper connection between the brand and the consumer. Conclusion Predictive analytics has become an indispensable tool in enhancing the consumer experience in e-commerce. By leveraging data and AI, businesses can offer personalized shopping experiences, anticipate customer needs, optimize inventory and delivery processes, and improve pricing strategies. The ability to predict consumer behavior and respond proactively to their needs not only increases customer satisfaction but also drives business growth and loyalty. As e-commerce continues to evolve, predictive analytics will play an increasingly vital role in shaping the future of the industry. Businesses that embrace this technology and leverage its full potential will have a competitive edge in delivering exceptional consumer experiences that keep customers coming back. Predictive analytics is not just about forecasting future trends; it’s about creating a future where businesses and consumers are better connected, and where personalized experiences lead to stronger, more lasting relationships.
Copyright (c) 2024 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
Latency Optimization in Edge vs. Cloud Computing: A Comparative Study for Real-Time Applications
Background and Purpose: The increasing demand for real-time applications such as autonomous vehicles, industrial automation, and telemedicine has driven the need for low-latency computing solutions. While traditional cloud computing offers extensive computational power, it suffers from latency due to long-distance data transmission. In contrast, edge computing reduces latency by processing data closer to the source, despite facing resource limitations. This study is designed to compare latency optimization strategies in edge versus cloud computing to support the unique demands of real-time applications. Methods: The research employs a comparative analysis framework that focuses on key performance metrics and real-world case studies. It evaluates various optimization techniques including caching, network slicing, AI-driven workload allocation, and data compression. This approach allows for a systematic assessment of the latency performance and resource efficiency of both computing paradigms. Findings: Results indicate that edge computing substantially reduces latency by minimizing the distance data must travel, though it is constrained by limited resources. In contrast, cloud computing, while offering high computational capabilities, introduces latency overhead due to extended data transmission. The findings suggest that hybrid models, which integrate edge and cloud computing, provide the most effective balance by leveraging the strengths of each paradigm to enhance overall system performance. Theoretical Contributions: This study advances the theoretical understanding of distributed computing architectures by clarifying the inherent trade-offs between latency reduction and resource availability. It contributes a novel framework for evaluating hybrid edge-cloud models, thereby enriching the academic discourse on real-time system optimization. Conclusions and Policy Implications: The research concludes that hybrid computing models offer a promising solution for real-time applications by combining low latency with scalable computing power. Policymakers and industry leaders are encouraged to consider integrated edge-cloud infrastructures in future technology deployments, as these models can enhance performance while addressing the limitations of each individual approach.
Sustainable Business Practices: Integrating Environmental, Social, and Governance (ESG) Metrics
Background and Purpose: Sustainability has become a crucial aspect of corporate strategy, driven by increasing stakeholder expectations, regulatory requirements, and consumer demand for responsible business practices. Environmental, Social, and Governance (ESG) metrics serve as a framework for assessing corporate sustainability and ethical impact. This study examines the role of ESG metrics in business management, their influence on corporate performance, and the challenges and opportunities associated with their adoption. Methods: The paper employs a multidisciplinary approach, integrating insights from existing literature, case studies, and empirical data to analyze the implementation of ESG metrics. It explores corporate governance structures, performance indicators, and regulatory frameworks to assess how businesses integrate ESG principles into their operational models. Findings: The study reveals that integrating ESG metrics enhances long-term business sustainability by improving risk management, stakeholder engagement, and financial performance. Companies that align their strategies with ESG principles tend to experience increased investor confidence and competitive advantage. However, challenges such as standardization issues, reporting complexities, and resistance to change hinder widespread adoption. Theoretical Contributions: This research contributes to the growing body of literature on sustainable business practices by offering a comprehensive framework for understanding ESG integration in corporate governance. It highlights the interconnectedness of ESG dimensions and their impact on business resilience and value creation. Conclusions and Policy Implications: The findings underscore the importance of regulatory support and standardized ESG reporting frameworks to enhance transparency and accountability. Policymakers and corporate leaders must collaborate to establish clear guidelines and incentives for ESG adoption, ensuring long-term sustainability and ethical business practices.
AI-Powered Decision-Making: Balancing Automation and Human Oversight in Corporate Governance
Background and Purpose: The integration of Artificial Intelligence (AI) in corporate governance has significantly transformed decision-making processes, enhancing efficiency, transparency, and predictive accuracy. AI-driven analytics, machine learning algorithms, and robotic process automation (RPA) have enabled organizations to optimize risk management, regulatory compliance, and strategic planning. However, concerns regarding ethical accountability, algorithmic biases, and the diminishing role of human oversight necessitate a critical examination of AI’s role in governance. This study aims to explore the benefits and risks associated with AI adoption in corporate governance and highlight the need for a balanced approach that combines automation with human judgment. Methods: This paper employs a multidisciplinary approach, drawing from recent literature, case studies, and empirical analyses to assess the impact of AI on corporate governance. It examines the applications of AI in governance structures, identifies key challenges, and evaluates existing regulatory frameworks governing AI adoption in corporate decision-making. Findings: The study finds that AI-driven governance enhances operational efficiency, reduces compliance risks, and improves data-driven decision-making. However, over-reliance on AI introduces ethical concerns, including biases embedded in algorithms, lack of transparency in decision-making, and the potential marginalization of human judgment in critical corporate affairs. The findings underscore the necessity of hybrid governance models that integrate AI-driven insights with human expertise to mitigate risks and ensure responsible decision-making. Theoretical Contributions: This paper contributes to corporate governance and AI ethics literature by proposing a framework for responsible AI adoption that balances automation with human oversight. It extends existing discussions on algorithmic accountability and governance ethics by emphasizing the role of regulatory mechanisms and ethical AI frameworks in ensuring fair and transparent corporate decision-making. Conclusions and Policy Implications: To harness AI’s potential while mitigating associated risks, organizations must implement governance models that combine AI-driven insights with human expertise. Regulatory bodies should establish comprehensive frameworks to address algorithmic biases, ensure transparency, and uphold ethical standards in AI adoption. A balanced governance approach will foster trust, sustainability, and long-term value creation in corporate decision-making.
Role of AI in Business Framework Revolution in Developed Countries
Background and Purpose: The integration of Artificial Intelligence (AI) into business frameworks has transformed operations, decision-making, and customer engagement, particularly in developed economies. This study explores the profound impact of AI on businesses by examining advancements in automation, data analytics, and strategic decision-making. It also investigates AI-driven business models, associated challenges, ethical considerations, and the evolving regulatory landscape. Methods: This research employs a qualitative approach, utilizing case studies of AI adoption across various industries. It analyzes secondary data from academic literature, industry reports, and regulatory guidelines to assess AI's role in enhancing business efficiency and competitiveness. Findings: The study reveals that AI significantly improves business performance by automating processes, optimizing data-driven strategies, and enabling innovative business models. However, challenges such as data privacy concerns, algorithmic bias, and regulatory complexities persist. The research highlights how businesses navigate these challenges while leveraging AI to gain a competitive advantage. Theoretical Contributions: This paper contributes to the existing literature by providing a comprehensive framework for understanding AI’s role in business transformation. It integrates perspectives from technology adoption theories, competitive strategy, and regulatory compliance, offering a holistic view of AI’s impact on business sustainability and growth. Conclusions and Policy Implications: The study concludes that AI is a critical driver of business innovation and efficiency but requires balanced regulatory oversight to address ethical and operational risks. Policymakers must develop adaptive frameworks that encourage AI-driven growth while ensuring responsible implementation. Businesses should invest in AI governance and ethical AI practices to sustain long-term success in evolving market landscapes.