Journal of Economics & Management Research
Journal of Economics & Management Research is an open access peer-reviewed journal from the publishers of Scientific Research and Community (SRC) which publishes articles in the form of written, video and power-point presentations from the arena of Economics & Management Research. The journal focuses to build up accessing the complete content of articles freely from online for reader’s perusal. Article acceptance will be in the form of Research, Review, Case Reports, Analysis, Magazines, Editorials, Opinion, Communications, Mini-Review, Short Communications, Picturized, Book Review, Video type and Power Point Presentations (PPTS). You can submit your article through online: https://www.onlinescientificresearch.com/submit-online or through Email attchment: economics@onlinescientificresearch.info 1.5 Impact Factor 84.5% Citations 90% Acceptance
Banking Innovations and Agricultural Sector Performance in Nigeria
The study investigated banking innovations and agricultural sector performance in Nigeria. An Ex-Post Facto research design was adopted, utilizing monthly time-series data from 1980 to 2024, sourced from the Central Bank of Nigeria and the Nigerian Bureau of Statistics. The data were analyzed using descriptive statistics and the Autoregressive Distributed Lag (ARDL) model to capture both short-run and long-run dynamics. The study's diagnostic tests confirmed the stability and reliability of the models. The study's ARDL long-run analysis revealed that ATM transactions had a significant positive impact on agricultural GDP with a coefficient of 0.004691 (p = 0.0000), while EFT transactions had a significant negative effect with a coefficient of -0.015411 (p = 0.0008). Mobile banking and POS transactions had no significant long-run impact. In the short run, negative lag effects of ATM and mobile banking were observed, while EFT showed positive short-run contributions. The error correction term indicated that 39.4% of disequilibrium was corrected each period. The model also passed diagnostic tests for serial correlation, heteroskedasticity, and model specification, confirming its reliability. The study concluded that while ATM transactions support agricultural growth, mobile banking, POS, and EFT have mixed or negative impacts on agricultural productivity. It recommended that policies should focus on improving the integration of digital banking tools with the agricultural sector and address structural constraints such as low digital literacy and weak rural infrastructure.
Autoregressive Distributed Lag (ARDL) Model
Copyright (c) 2026 Journal of Economics & Management Research
U.S. Semiconductor Export Structure under Innovation and Trade Policy
Semiconductors are a foundational input for modern economic activity, and their international trade is shaped by both market forces and government policy. This study examines how U.S. innovation and trade policies are associated with changes in the structure of semiconductor exports, focusing on shifts in export allocation, destination concentration, and specialization rather than export volumes alone. Using bilateral UN Comtrade data classified by harmonized system (HS) codes from 2015 to 2024, the analysis applies measures of export concentration and comparative export strength to assess changes in market dependence and positioning. The results indicate that major policy changes coincide with export reallocation across destination markets, while U.S. exports remain concentrated in technologically advanced segments. Overall, the findings link policy environments to observable changes in export structure in a technology-intensive industry.
African Continent Free Trade Area Development Strategy Analysis
The establishment of the African Continental Free Trade Area (AfCFTA) will promote the agglomeration of industries in the African region, the extension of the industrial chain and the growing economies of scale, and promote the participation of African countries in the global supply chain. From the perspective of how Africa should deal with the difficulties in the development process of the free trade area, this paper discusses how the development strategy and strategy of the free trade area on the African continent can help its stable development from the aspects of the construction of the free trade area on the African continent should learn from China's experience, promote the development of African port economy, help the development of agriculture and industrialization in Africa, increase and improve the investment in infrastructure, and connect the development of urbanization in Africa.
Copyright (c) 2024 Journal of Economics & Management Research
Building a Tariff-Proof Economy: Ecosystem Rules for A Sustainable Economic Future
Humanity suffered a wide range of health concerns until we gained understanding of the inner-workings of the human body, and the nature of pathogens that threaten it. We lack an equivalent understanding of our economy, leaving many nations vulnerable to supply chain disruptions, volatile energy markets, declines in soil fertility, shifting global trade policy, and a litany of other social and environmental challenges. These challenges will likely remain unresolved until we address the economic flaws responsible for their creation. Drawing from thirty years of research and practice in business, economics, and ecology, this seminar outlines three foundational components governing sustainable, tariff-proof, economies—diversity, energy, and trade. The identification of these foundational (governing) components, and the dynamic relationships between them, is the central theme of this talk. This talk presents a fresh perspective on the structure of a sustainable economy, as well as traditional and nontraditional measures of economic productivity, the role of economic succession in stimulating productivity, an ecological explanation of the wealth gap, and the natural geography of a sustainable economy. Rather than perceive tariffs and volatile energy markets as threats, how might nations navigate around such forces while increasing GDP and resilience. The talk closes with a path for economic restoration, with case studies from the U.S., Ecuador, Sri Lanka, Italy, and elsewhere. Policy implications are provided for national and local levels. To guide the public’s attention and resources toward building a sustainable economy, and to catalyze change, the structure of a sustainable economy must first be understood. And the paths toward economic restoration must be clearly visible.
Copyright (c) 2025 Journal of Economics & Management Research
Advancing Scalable and Secure AI Systems: A Decade of Innovation in AI Infrastructure, MLOps, and Data EngineeringSafeguarding Financial Data
An AI and MLOps innovator with a decade of experience in designing and deploying scalable, secure, and intelligent AI-driven systems. This work focuses on pioneering advancements in AI infrastructure, MLOps automation, and cloud-scale data platforms that empower organizations to operationalize machine learning with efficiency and confidence. With a strong foundation in data engineering, the approach emphasizes building end-to-end pipelines that ensure data integrity, model reproducibility, and seamless deployment. Key contributions include the integration of robust CI/CD frameworks for ML, scalable data ingestion and processing architectures, and governance mechanisms to support compliance and security. This journey reflects a commitment to driving the next generation of intelligent systems by aligning technological innovation with real-world business outcomes.
Comprehensive Guide to Insurance and AAA Membership Benefits: What You Need to Know Safeguarding Financial Data
In today’s fast-paced world, protecting what matters most your family, assets, and peace of mind is more important than ever. From auto and home insurance to specialized coverage like RV, boat, and event insurance, understanding your options is key to making informed decisions. This guide also explores the valuable perks of AAA membership, including roadside assistance, travel services, and exclusive discounts. Whether you’re securing your future with life insurance or planning for the unexpected with long-term care and annuities, this comprehensive overview breaks down the essentials to help you stay protected and prepared.
Transforming the Automotive Industry: From Manufacturing and Mobility to AI, Cloud, and Sustainable SolutionsSafeguarding Financial Data
The automotive industry is undergoing a profound transformation driven by rapid advancements in technology, evolving consumer expectations, and increasing pressure for environmental sustainability. This paper explores the comprehensive evolution of the sector, encompassing vehicle manufacturing and distribution, Research & Development (R&D), sales and marketing, and financial services. It delves into the integration of connected services and next-generation mobility solutions, such as ride-sharing and autonomous driving. A key focus is placed on the role of emerging technologies Artificial Intelligence (AI), Machine Learning (ML), data engineering, and cloud computing platforms like AWS and Azure in revolutionizing automotive operations. These technologies are enabling predictive maintenance through “engine heartbeat” monitoring, enhancing customer engagement, optimizing supply chains, and accelerating innovation cycles. Additionally, the paper highlights environmental sustainability initiatives, examining how automakers are leveraging digital tools and green technologies to reduce emissions, improve energy efficiency, and meet global regulatory standards. By analyzing the convergence of traditional automotive practices with cutting-edge digital ecosystems, this paper provides a strategic overview of how the industry is reshaping itself for a smarter, more connected, and sustainable future.
AI-Enhanced Chatbots for Real-Time Symptom Analysis and Triage in Telehealth Services Safeguarding Financial Data
AI-enhanced chatbots are transforming telehealth services by enabling real-time symptom analysis and automated patient triage. Traditional healthcare systems often face challenges such as long wait times, resource constraints, and accessibility barriers. AI-driven chatbots leverage Natural Language Processing (NLP) and Machine Learning (ML) to analyze patient symptoms, assess risk levels, and provide preliminary medical guidance. These intelligent systems enhance telehealth by streamlining patient intake, prioritizing urgent cases, and reducing the burden on healthcare providers. Additionally, chatbot-driven triage improves patient engagement, ensures timely interventions, and facilitates remote healthcare access. However, challenges such as diagnostic accuracy, data privacy, and regulatory compliance must be addressed to ensure ethical and effective deployment. This study explores the capabilities, benefits, and limitations of AI-powered chatbots in telehealth, highlighting their potential to improve efficiency, accessibility, and patient outcomes in modern healthcare ecosystems.
The Role of Business Interruption Insurance in Economic Resilience: A Post-Pandemic Analysis of Policyholder Compensation and Legal ChallengesSafeguarding Financial Data
Business Interruption (BI) insurance plays a critical role in economic resilience by providing financial protection to businesses facing operational disruptions. This study examines the effectiveness of BI insurance in compensating policyholders during crises, with a focus on post-pandemic recovery. It analyses key challenges, including ambiguities in policy coverage, legal disputes over pandemic-related claims, and the role of government interventions in supporting affected businesses. The research explores how insurers and policymakers can enhance BI insurance frameworks to improve risk assessment, policy clarity, and claims processing. Additionally, emerging solutions such as parametric insurance models and AI-driven risk analytics are discussed to address future economic uncertainties. By evaluating the lessons learned from the COVID-19 pandemic, this study provides insights into strengthening BI insurance as a tool for fostering economic stability and business continuity in the face of global disruptions.
AI-Driven Treasury Management: Reinforcement Learning Models for Liquidity Optimization and Fraud Prevention in Large-Scale Financial Institutions
AI-driven treasury management is transforming financial institutions by leveraging reinforcement learning models to optimize liquidity and enhance fraud prevention. This study explores how AI-powered algorithms analyze real-time financial data, predict cash flow trends, and dynamically allocate liquidity to maximize capital efficiency. Reinforcement learning enables adaptive decision-making, allowing treasury systems to respond proactively to market fluctuations, interest rate changes, and regulatory requirements. Additionally, AI-driven fraud detection mechanisms use anomaly detection and predictive analytics to identify suspicious transactions, mitigate risks, and strengthen financial security. The research highlights key challenges, including data privacy, model interpretability, and integration with legacy financial systems, while proposing strategies to enhance AI adoption in treasury management. By combining reinforcement learning with big data and cloud computing, large-scale financial institutions can achieve greater operational resilience, improved risk management, and a more agile approach to liquidity optimization and fraud prevention.
The Future of Digital Financial Security: Integrating AI, Cloud, and Big Data for Fraud Prevention and RealTime Transaction Monitoring in Payment Systems Safeguarding Financial Data
The rapid evolution of digital payment systems has heightened the need for advanced security measures to combat financial fraud and ensure real-time transaction monitoring. This study explores the integration of Artificial Intelligence (AI), cloud computing, and big data analytics in enhancing digital financial security. AI-driven fraud detection models leverage machine learning algorithms to identify suspicious patterns, mitigate risks, and automate threat response mechanisms. Cloud computing provides scalable infrastructure for secure data processing, enabling seamless real-time transaction monitoring and fraud prevention. Big data analytics enhances predictive modelling, allowing financial institutions to detect emerging fraud trends and adapt security protocols dynamically. Additionally, this research examines challenges such as data privacy, regulatory compliance, and adversarial AI risks while proposing strategies for strengthening financial cybersecurity. By integrating AI, cloud, and big data, the future of digital financial security promises greater transparency, resilience, and consumer protection in an increasingly digital economy.
AI-Driven Identity Protection: The Intersection of Gen AI, Credit Monitoring, and Fraud Alerts in Safeguarding Financial Data
As financial fraud and identity theft threats continue to rise, AI-driven solutions are becoming essential for safeguarding financial data. This study explores the intersection of Generative AI (Gen AI), credit monitoring, and fraud alerts in enhancing identity protection. AI-powered models analyze vast datasets in real time to detect anomalies, predict fraudulent activities, and automate rapid response mechanisms. By leveraging Gen AI, financial institutions can improve authentication processes, personalize fraud prevention strategies, and enhance anomaly detection through adaptive learning. Additionally, AI-driven credit monitoring systems offer proactive alerts, enabling consumers to take preventive action against potential breaches. This research also examines challenges such as data privacy, ethical AI implementation, and the risk of adversarial attacks. By integrating AI with financial security frameworks, organizations can strengthen consumer trust, mitigate financial risks, and establish a more resilient, data-driven approach to identity protection.
Analyzing Behavioral Trends in Credit Card Fraud Patterns: Leveraging Federated Learning and Privacy-Preserving Artificial Intelligence Frameworks
As credit card fraud becomes increasingly sophisticated, financial institutions must adopt advanced security measures to detect and prevent fraudulent activities while ensuring user privacy. This study explores the use of Federated Learning (FL) and privacy-preserving Artificial Intelligence (AI) frameworks to analyse behavioural trends in credit card fraud patterns. By leveraging FL, financial institutions can train AI models collaboratively across decentralized datasets without exposing sensitive customer information. Machine learning algorithms detect anomalies, predict fraud attempts, and adapt to emerging attack strategies in real time. Additionally, privacy-preserving AI techniques such as differential privacy and homomorphic encryption enhance data security while maintaining model accuracy. This research also examines challenges related to scalability, computational efficiency, and regulatory compliance in implementing FL-based fraud detection systems. By integrating AI-driven fraud analysis with privacy-preserving technologies, financial institutions can improve fraud prevention strategies, strengthen cybersecurity, and enhance consumer trust in digital transactions.
Lakshminarayana Reddy Kothapalli Sondinti
Transformative Applications of Generative AI in Socially Responsible Financial Models: A Machine Learning Perspective
Generative AI is redefining socially responsible financial models by enhancing decision-making, risk assessment, and financial inclusion. This study explores the transformative applications of Generative AI in developing ethical, transparent, and sustainable financial systems from a machine learning perspective. Traditional financial models often struggle with biases, inefficiencies, and limited adaptability to dynamic economic conditions. By leveraging Generative AI, institutions can create predictive models for impact investing, fraud detection, credit risk evaluation, and financial forecasting, ensuring fairer and more inclusive financial practices. Additionally, AI-driven simulations enable scenario analysis for sustainable investments, helping organizations align with Environmental, Social and Governance (ESG) goals. However, challenges such as algorithmic bias, data privacy, and regulatory compliance remain critical considerations. This paper examines the potential and limitations of Generative AI in fostering ethical finance, emphasizing the need for responsible AI deployment to enhance transparency, efficiency, and accessibility in financial systems.
Photonic Data Centers: Advancing Energy Efficiency and Speed in Next-Generation Cloud Infrastructure
The growing demand for high-performance cloud computing has intensified the need for faster, more energy-efficient data centres. Photonic data centres, leveraging optical computing and fibre-optic communication, offer a transformative solution by significantly reducing energy consumption while increasing data transmission speeds. Unlike traditional electronic-based data centres, photonic architectures minimize latency, heat dissipation, and power usage, making them ideal for scalable and sustainable cloud infrastructure. This paper explores the role of silicon photonics, optical interconnects, and quantum photonic processors in enhancing computational efficiency, bandwidth, and AI-driven workloads. Additionally, we address challenges such as integration complexities, cost constraints, and the need for new optical networking protocols. As the demand for AI, big data, and real-time analytics grows, photonic data centres emerge as a key enabler of next-generation cloud computing, driving innovation toward ultra-fast, low-power, and environmentally sustainable digital infrastructure.
The Impact of Geopolitical Tensions and The Divergent Trajectories of Monetary Policies
This article analyzes the impact of geopolitical tensions and divergent monetary policy trajectories over the period 2010-2024. Using an econometric framework, the study shows that these geopolitical tensions exert both upward and downward pressure on policy rates, reflecting an active adaptation of monetary policies to external shocks in a context of increasing uncertainty. These responses can be interpreted as preventive measures against inflationary risks or as a signal of caution. However, the results reveal significant heterogeneity between countries, reflecting divergent monetary policy trajectories shaped by the specific institutional, economic, and political characteristics of each state. Although the explanatory power of the model remains moderate, the study's conclusions offer valuable insights for policymakers and international institutions facing an increasingly unstable geopolitical environment.
Artificial Intelligence and the Transformation of the Growth Employment Nexus: An Empirical Reappraisal of Okun’s Law
This paper examines the impact of artificial intelligence (AI) and technological progress on the relationship between economic growth and unemployment, traditionally described by Okun’s law. Using panel data for 11 developed countries over the period 2000–2024, the analysis relies on fixed-effects models and dynamic specifications estimated through the System Generalized Method of Moments (System-GMM). Technological intensity is proxied by an information and communication technology (ICT) index, and an interaction term is introduced to assess its moderating role in the growth–employment relationship. The results confirm the short-run validity of Okun’s law, as economic growth exerts a negative and statistically significant effect on changes in unemployment. However, technological intensity has a positive direct effect on unemployment and weakens the ability of growth to reduce unemployment, suggesting adjustment costs related to automation. Overall, the findings point to a structural transformation of the growth–employment nexus and highlight the need for active policies in skills development and labor market adjustment to ensure more inclusive growth.
Empowering Change from the Ground Up: Women’s Entrepreneurship in Sub-Saharan Africa’s Informal Sector
Women’s entrepreneurship in Sub-Saharan Africa (SSA) is a critical driver of socio-economic development, within the informal sector where women enterprises are concentrated. Despite substantial contributions, structural and institutional barriers impede the formalization of these businesses, curbing their economic impact. This study examines how women entrepreneurs in Ghana and SSA navigate informal practices to achieve economic mobility. Employing a systematic literature review - PRISMA framework, the research synthesizes key themes including entrepreneurial capacity, sustainability, intellectual property, and institutional support. A comparative lens incorporating Rwanda and South Africa reveals Ghana’s high rate of female entrepreneurship, predominance of informal trade, and socio-cultural dynamics that shape entrepreneurial trajectories. The study advances discourse on informal entrepreneurship by highlighting its necessity-driven nature and underscoring the pivotal role of policy environments in facilitating formalization. The findings offer actionable insights for policymakers and development practitioners aiming to strengthen support systems and promote inclusive economic growth across the region.
Artificial Intelligence and the Global Bioeconomy-Optimizing the International Competitiveness of Plant-Based Bioproducts
The global bioeconomy is undergoing a profound transformation, with plant-based bioproducts emerging as key drivers of sustainable development and international trade. As demonstrate, the circular bioeconomy represents a market opportunity exceeding $400 billion, where artificial intelligence (AI) serves as a critical enabler for value chain optimization and market integration.
Positionless Marketing Theory, Positionless Marketing Strategy and Positionless Marketing Principles
The term Positionless Marketing was first coined by Saurav Kumar in his researches on marketing and consumer behaviour particularly based on artificial intelligence. The Marketing has evolved beyond static roles and siloed teams. In today’s hyper-dynamic environment, customer expectations demand more: speed, relevance, empathy, and personalization. That is where the Positionless Marketer thrives. Enabled by Positionless Marketing, modern marketers are no longer confined to the limitations of marketing assembly lines. They can now execute any marketing task instantly and independently, from audience segmentation to campaign creation to performance optimization. While this shift is powered by AI, automation, and real-time data, the underlying success still depends on one thing: understanding people. This is where Dale Carnegie’s timeless book: “How to Win Friends & Influence People,” published in 1936, becomes surprisingly modern. With more than 30 million copies sold, Carnegie’s principles remain essential for anyone trying to build meaningful relationships, including marketers. At its core, Positionless Marketing is about just that: forging genuine, personal connections at scale. Research Objective: How the core habits from Carnegie’s classic, map directly to the practice of Positionless Marketing and provides Positionless marketing theory, Positionless marketing strategy and Positionless marketing principles. Research Methodology: The study explains by using Dale Carnegie’s habits how positionless marketing can be emphasized and provides Positionless marketing theory, Positionless marketing strategy and Positionless marketing principles. Originality Value: The research first to foster a unique way of explaining the positionless marketing.
Financial Time Series: Adaptive Forecasting Frameworks
This paper analyzes various machine learning techniques applied to complex financial time series data for predictive analytics. It details essential data preprocessing and feature engineering, followed by a comparative evaluation of diverse algorithms, highlighting their effectiveness in dynamic market forecasting. The iterative evaluation process demonstrates a robust modeling feedback mechanism crucial for optimizing predictive accuracy in volatile environments. This work provides insights valuable for advancing Business with AI strategies, particularly in financial decision-making and risk management.
Economic Vulnerability, Land Use Planning and Human Capital in the West African Economic and Monetary Union (WAEMU): Analysis based on PASEC Data
This article examines the factors that influence the human capital of primary school students in six WAEMU countries, considering both the economic vulnerability of households and land use planning. Drawing on theories of human capital, intergenerational poverty, and educational resilience, it uses PASEC 2019 data and adopts a three-level multilevel model supplemented by a binary logit model to analyze factors of educational resilience. The results show that economic vulnerability significantly reduces human capital, while better spatial planning and favorable educational practices (preschool education, interest in reading, urban or private schooling) strengthen it. The study highlights the importance of an integrated approach combining social, educational, and territorial policies to reduce educational inequalities and strengthen human capital in the WAEMU region.
Exploring Financial Motives for Earnings Management in the Economic Crisis. Evidence from Greek and German Listed Companies
The study examines the impact of economic crisis and focuses on the reasons for cases of earnings management. More specifically, it analyses whether executive compensation, analysts’ forecasts and violation of debt covenants are important motives for accounting fraud. It also examines whether voluntary disclosure of financial information could improve the quality of financial information or confuse investors. The paper examines listed companies in the Greek and German stock markets from 2007 to 2009 to find out how these two economies reacted after the introduction of IFRS. Thus, it would be interesting to find out whether Germany, a strong EU economy, was able to eliminate earnings management motives, as most would predict, while also examining whether Greece, a weak economy, was able to achieve similar results. The results of the study provide useful insights as they confirm that companies that provide additional accounting disclosures perform better in both countries. However, the remaining results suggest that credit covenant violations, executive compensation and bonuses, and analyst forecasts remain strong motivators for earnings management in both countries studied. In this order, it seems that IFRS need to introduce and maintain improved mechanisms to overcome such phenomena.
The Ethical Implications of Artificial Intelligence in Decision-Making: Balancing Innovation with Accountability and Integrity
As Artificial Intelligence (AI) technologies become increasingly embedded in critical organizational decision-making processes, questions of ethics, accountability, and integrity have risen to the forefront of academic and industry discourse. This study investigates the ethical implications of AI in decisionmaking, with a specific focus on how organizations can balance technological innovation with moral responsibility and regulatory accountability. Guided by an integrated theoretical framework that combines Ethical Decision-Making Theory, Stakeholder Theory, the Technology Acceptance Model (TAM), and the Responsible Innovation Framework, this research provides a multidimensional perspective on AI ethics in practice. A quantitative research methodology was employed, gathering data from 395 stakeholders including AI developers, corporate leaders, ethics compliance officers, public regulators, data scientists, and end-users across sectors such as finance, healthcare, education, and government services. The study examines stakeholder perceptions on key ethical dimensions of AI implementation—algorithmic transparency, data privacy, bias mitigation, informed consent, accountability mechanisms, and the inclusiveness of AI design. A structured survey instrument was used to assess how these variables influence organizational trust, adoption willingness, and public acceptance of AI-driven decisions. The findings are expected to demonstrate that organizations that embed ethical safeguards and transparent governance frameworks within their AI systems can significantly enhance stakeholder trust, user engagement, and regulatory compliance. The study also highlights critical challenges, including the opacity of black-box algorithms, the absence of global ethical standards, the underrepresentation of marginalized groups in algorithmic training data, and the tensions between innovation speed and ethical reflection. Additionally, it explores how external institutional pressures—such as media scrutiny, customer expectations for ethical AI, investor demand for ESG-compliant practices, and evolving legal mandates—act as catalysts for adopting ethical AI governance models. This research makes several key contributions. First, it bridges a significant gap in the empirical literature on AI ethics by quantitatively analyzing perceptions across diverse industries and stakeholder categories. Second, it offers a conceptual model for balancing innovation with ethical accountability that can guide practitioners, policymakers, and technologists in AI strategy formulation. Third, it provides a framework for integrating responsible AI principles into corporate governance and digital transformation strategies. The study ultimately advocates for the co-evolution of ethical standards and AI capabilities to ensure that technological progress does not come at the expense of human values, social justice, or democratic integrity
Circular Organization of Economic Processes Symmetric Model of Economic Equilibrium
On the basis of dialectical analysis of basic economic categories a new understanding of economic value, price formation and general equilibrium has been proposed. Market economy appears as the form of a complex, non-linear, functionally closed and causally open system of economic actions. Such systems have a number of unique properties that are well studied by second-order cybernetics. The self-organization of a market economy is carried out through the recursive processes. Recursive processes in the economic system, as well as in other complex nonlinear dynamical systems, generate "eigenvalues" ("fixed points"). These "eigenvalues" are the equilibrium prices and quantities of goods to which through the recursive processes tend the actual prices and quantities, thus providing a tendency to the general equilibrium. However, due to constant influence on the system of random external factors, the general equilibrium is never achieved. On the base of created model the hidden relationships among the gross profit, gross saving, gross investment and gross consumption in debt, as well as the relationships among the other economic parameters are revealed. "Circularity is not a flaw but a feature. It transforms a vicious circle into a creative one." Heinz von Foerster "The whole is greater than the sum of its parts. Systemic properties are emergent and cannot be predicted from the properties of individual components." Ludwig von Bertalanffy "Production is also immediately consumption, consumption is also immediately production." Karl Marx
Inherited Family Trauma Model and Its Application in Jesmyn Ward's Sing, Unburied, Sing
Jesmyn Ward’s Sing, Unburied, Sing is a polyvocal narrative that touches on death, institutional racism and multi-generational trauma. The novel explores the trials and tribulations of a broken Mississippi family through the perspective of a twelve year old boy named Jojo, his mother Leonie, and a ghost by the name of Richie. Inherited family trauma is at the forefront of this novel as it unveils the effects of slavery and how stress factors and behavioral responses translate across generations.
Research on Development of Software Industry and Intelligent Industry in Taiyuan City
This research report focuses on the theme of development of software industry and intelligent industry in Taiyuan City. Through on-site inspections of local enterprises in Taiyuan and demonstration parks and innovation bases in advanced areas such as Beijing, Suzhou, Wuxi, and Nanjing, the current situation, existing problems, and future paths of software and intelligent industry development in Taiyuan City are analyzed. The report points out that Taiyuan City has achieved certain results in policy support, infrastructure, and talent cultivation, but compared with developed areas, there are still problems such as incomplete industrial ecology, small enterprise scale, insufficient cross-border integration, and talent shortage. To this end, the report proposes suggestions such as strengthening top-level design, optimizing industrial layout, focusing on key areas (such as artificial intelligence and industrial software), and improving element guarantee. It also draws on the experience of Suzhou Nano city, Wuxi Xue Lang Town, Nanjing Software Valley, etc., to provide feasible solutions for Taiyuan to build a new highland for the development of software and intelligent industries in the central region.
The Effect of Strategic Planning and Business Performance of Micro-Small and Medium Enterprises in Kaduna State of Nigeria
The overarching objective of this study is to measure the effect of strategic planning on performance of Micro-Small and medium enterprises in Kaduna state as focal point. In recent times, entrepreneurial competencies and strategic planning dimension’s role has been acknowledge as prime and imperative factors and tools for MSMEs performance. In spite of this imperative factors there are insufficient research evidence on the roles, values and produce of strategic planning for MSMEs. Past studies has also shown that many researchers use one dimensional measure of strategic planning, which is not in line with the multi-dimensional nature of strategic planning. This present study therefore intends to close this gap. This study aims to identify the entrepreneurship competency and strategic dimensions that an entrepreneur needs to acquire to operate his/her business successfully particularly the owners of the enterprises. The study used both survey and descriptive research designed and questionnaire was distributed to MSMEs in Kaduna Metropolis. The study population for this study was 480 owners of MSMEs in Kaduna metropolis. The sample size was derived using taro Yamane and the sample was arrived at 234.however, 185 questionnaires was retrieved with only 133 were properly filled which was used as a base for the purpose of data presentation and analysis. The research model was interrogated using SPSS-21 version and results revealed that there is a significant positive relationship between strategic planning dimensions and entrepreneurial competencies on the performance of MSMEs. Both strategic planning dimensions and entrepreneurial competencies has positive relationship with performance of operators/owners of MSMEs in Kaduna Metropolis. In general, this finding supported the view that competency can override predisposition of individual entrepreneur. The study recommended that every MSMEs owners should engage in strategic planning process so as build the needed entrepreneur’s competency and create a synergy between the markets and the business activities for high performance.
Adaptation of Business Models in Innovative Startups: An Empirical Analysis within the LA START UP STATION Incubator
This article examines how innovative startups adapt their business models in an environment marked by high uncertainty. Based on a case study conducted with companies supported by the LA START UP STATION incubator, the analysis highlights an iterative adjustment process relying on approaches such as Lean Startup, Effectuation, and Design Thinking. The results emphasize the importance of experimentation, market feedback, and strategic flexibility to ensure product-market fit.
The Role of Fossil Fuel Subsidy Reforms in the Nexus Between Fossil Fuel Consumption and Economic Growth in Nigeria: Evidence fromTwo-Stage Least Squares (2SLS) and Granger Causality Approaches
The fossil fuel reformes has become a global imperative strategy to achieve the modern economic growth objectives in the face of rapid changes in industrial revolution, and socio-economic challenges, with focus to the developing economies. This motivated the empirically analysis of the role of fossil fuel subsidy in the nexus between fossil fuel consumption and economic growth in Nigeria from 1973 to 2024. This Study employs a Two stage least squares (2SLS) to resolve the endogeneity problem in the previous studies and also estimate ordinary least squares and Granger causality as a robustness check for the existing studies. Findings reveal that a 1% increase in PMS fuel consumption through the instrumental variables during the partial fuel subsidy removal has increased economic growth by 37.22% whereas, a 1% increase in PMS fuel consumption through the instrumental variables during the full fuel subsidy removal has decreased the economic growth by 0.34% in this study. This study recommends emphasize on premium motor spirit price, exchange rate, number of refineries, and real GDP per capita in the pursuit of fuel subsidy reform to directly and indirectly promotes fuel consumption and economic growth by the Nigeria energy stakeholders, especially the government.