International Journal of Finance, Economics, and Management Studies
International Journal of Finance, Economics, and Management Studies (IJFEMS) is a scholarly platform that typically publishes high-quality, peer-reviewed research focused on the latest developments and emerging trends in the fields of finance, economics, and management. The journal aims to foster intellectual exchange among researchers, policymakers, and professionals in these disciplines, offering insights into both theoretical and practical aspects of the subjects. The main objective of IJFEMS is to offer a worldwide platform for the dissemination of new research in finance, economics, and management. The journal emphasizes interdisciplinary studies and stimulating new research integrating these disciplines to solve both emerging issues and enduring problems of the global economic and business environment.
Digital Transformation in SMEs: A Comparative Study of Developing and Developed Economies
Digital transformation has become a critical driver of competitiveness, productivity, and resilience for small and medium-sized enterprises (SMEs). However, the extent and impact of digital transformation vary significantly between developing and developed economies due to differences in infrastructure, skills, institutional support, and resource availability. The purpose of this study is to comparatively examine the drivers, adoption levels, challenges, and performance outcomes of digital transformation in SMEs operating in developing and developed economies. The study adopts a comparative research design using a mixed-methods approach. Quantitative data were collected through structured surveys administered to SMEs in selected developing and developed countries, while qualitative insights were obtained through semi-structured interviews with SME owners and managers. Statistical techniques, including descriptive analysis and regression modeling, were used to assess the relationship between digital transformation initiatives and firm performance. The findings reveal that SMEs in developed economies demonstrate higher levels of digital adoption, supported by advanced digital infrastructure, skilled labor, and stronger institutional frameworks. In contrast, SMEs in developing economies face significant barriers such as limited financial resources, inadequate digital skills, and weak technological ecosystems. Despite these challenges, digital transformation positively influences operational efficiency, market reach, and innovation in SMEs across both contexts. The study concludes that while digital transformation offers substantial benefits for SMEs regardless of economic context, targeted policy interventions, capacity-building initiatives, and affordable digital solutions are essential to bridge the digital divide and enhance SME competitiveness, particularly in developing economies.
Barriers to Technology Adoption Among Small and Medium Enterprises
This study examines the major barriers to technology adoption among small and medium enterprises (SMEs), focusing on financial, human, organizational, and infrastructural challenges that limit digital transformation. The goal of this research is to characterize and study the factors that prevent SMEs from adopting new technologies, and to evaluate how some of these barriers have an impact on their competitiveness and performance. A mixed-methods method is used, utilizing existing literature to form an argument supplemented by data collected from SME owners and managers using a survey, which is investigated in terms of description and themes. The results indicate that high implementation costs, lack of access to finance, insufficient technical capabilities, organizations’ resistance to change, poor infrastructure, and worry about data security are the major barriers to the adoption of technology. This research suggests that to support technology use and ensure the long-term growth of SMEs in an increasingly digital business space, addressing these challenges through supportive funded government policies, raising funding accessibility, capacity building on SME training, and improving digital infrastructure is paramount.
Factors Influencing Consumer Adoption of Digital Payment Systems: Exploring Demographic, Psychological, and Sustainability Dimensions
The financial landscape has been significantly altered by rapid advances in digital technology, with digital payment systems emerging as essential facilitators of financial transactions in modern economies. This research examines factors influencing customers' use of digital payment options, focusing on demographic, psychological, and sustainability characteristics. The study was quantitative, and the data were collected from 384 respondents using a standardized questionnaire design. The demographic characteristic indicated fairness across age, sex, and educational level. The relationships between the variables are evaluated with statistical techniques such as hypothesis testing and factor analysis. The findings show that invaluable customer, innovation, concern forthe environment, and sustainable business practices have a positive and significant influence on the acceptance of e-payment. Despite a negative correlation with perceived danger, the difference was not statistically significant. The research highlights the increasing importance of technological transparency and environmental consciousness in influencing customer choices. Implications of these findings apply to policymakers, Fintechs, and sustainability proponents in promoting digital payment adoption and fostering responsible consumption.
Driving Sustainable Growth: Exploring The Nexus Between Financial Innovation And Banking Sector Profitability In Nigeria
This paper investigates the effect of financial innovation on the profitability of the banking sector in Nigeria, with the new focus on the most popular mobile banking, digital payments, and blockchain technologies. It uses the data of 26 commercial banks available from the period of 2009 to 2024 to compare the profitability of them using return on assets ROA as the main measure. The data comes from the Central Bank of Nigeria CBN which contains digital innovations such as Web Pay, Mobile Pay, NIP, NEFT, Remita, and Central Pay. The results show that mobile banking and blockchain technologies have a more significant positive effect on profitability than the impact of digital payments which is different. The study urges strengthening mobile platforms, replacing the existing digital infrastructure with the new one, and implementing blockchain technologies.
Quantum Economics: Reframing Value, Scarcity, and Exchange in the Digital Age
This paper introduces Quantum economics as a post-classical economic paradigm capable of explaining value formation, exchange dynamics, and behavioural change in digital environments. Traditional economic assumptions inherited from classical and neoclassical frameworks fail to capture the dynamics of twenty-first-century markets, which are dominated by intangible assets, algorithmic trading, platform monopolies, and network-driven value creation. Drawing on principles from quantum theory including uncertainty, entanglement, superposition, relational statehood, and wave-function collapse this paper develops a conceptual and empirical model of economic reality that treats value probabilistically, contextualises identity, and recognises relational dependencies. The digital economy exemplifies structural uncertainty, non-linear cascading dynamics, network amplification, and valuation processes governed by expectations rather than intrinsic scarcity. By synthesising evidence from high-frequency finance, cryptocurrency valuation, data markets, and behavioural psychology, this article demonstrates that economic systems in the digital age operate as quantum information systems rather than classical mechanical systems. Comparative tables are developed to contrast classical and quantum economic assumptions and to map digital market phenomena to quantum concepts. The paper concludes by outlining a research programme integrating complexity theory, network mathematics, and behavioural macroeconomics, proposing Quantum Economics as a foundational paradigm for twenty-first-century economic science
Post-Scarcity Development for Emerging Economies: Towards Hybrid Prosperity Systems Beyond Economic Output Measures
This paper proposes a hybrid model of post-scarcity development for emerging economies that abandons traditional growth-output measures and instead focuses on regenerative socio-ecological capabilities, human flourishing, decentralised technological organisation, and long-term institutional resilience. Existing development models prioritise economic output measures that reduce complex well-being to quantifiable productivity indicators, thereby obscuring structural inequalities, ecological degradation, and human vulnerabilities (Costanza et al., 2014). This paper embeds post-scarcity thinking into African realities through a multi-systemic approach renewable energy networks, digital commons, platform cooperatives, knowledge economies, local autonomy, and innovation diffusion. The argument is supported by empirical research across development studies, ecological economics, political theory, and anthropology. It concludes that post-scarcity development must be neither anti-modern nor anti-market, but rather hybrid, pluriversal and systemic
Academic Leadership and Industry – Academia Collaboration: An Indian Perspective
Leadership is the concept of influencing others, whether fully or partially, by building their trust and motivating them verbally and in action. This does not necessarily imply authority or power. It can also refer to a leader's ability to influence power voluntarily delegated to them by members of a department. Academic leadership is a specific area of leadership within an educational environment or institution. In educational institutions, stakeholder value refers to the value of stakeholders such as students, staff, the community, and funding institutions. The various responsibilities of an excellent academic leader include administering the department, fulfilling the role of a faculty member, providing service and support to various constituencies (college, university, external community, associations), and fostering professional and personal development. This paper addresses various challenges in academic leadership, building organizations for change, industry-academic collaboration, and how to develop and nurture effective leadership among students in higher education
Opening the Ethiopian Banking Sector to Foreign Investors: Advantages and Disadvantages (A Literature Review)
For decades, the Ethiopian banking sector has remained closed to foreign investors. The government’s main rationale for restricting foreign participation was the concern that international banks being more financially robust, technologically advanced, and reputable would overshadow and weaken the growth of Ethiopia’s nascent and underdeveloped domestic banking industry. Recently, however, the government has enacted a new law permitting the entry of foreign banks into the country. This review examines the potential advantages and disadvantages of this policy shift. The anticipated benefits include the introduction of new financial innovations and technologies, increased foreign direct investment, accelerated economic growth, enhanced availability of foreign exchange, and greater facilitation of international trade. Moreover, foreign bank entry is expected to help alleviate Ethiopia’s persistent foreign exchange shortages. Nonetheless, significant risks exist if the reform is not carefully regulated. Without strong oversight by the National Bank of Ethiopia, the dominance of foreign banks may undermine local institutions and expose them to heightened vulnerabilities. The article concludes that while opening the banking sector presents important opportunities for Ethiopia’s economy, careful implementation and stringent regulatory frameworks are critical to safeguarding domestic banks and ensuring balanced, sustainable financial sector development
Climate Stress-Tested Factor Models: Extending Asset Pricing with ESG Risk
The financial market increasingly understands the climate challenge and ESG, or environmental, social, and governance, considerations, which have been disrupting traditional aspects of asset pricing and financial risk management. Standard factor models. Systematic risk premia and asset returns have long been explained by classical factor models, including, but not limited to, the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model, and its extensions. Nevertheless, such models are not particularly sensitive to ESG and climate-related threats, which are becoming more important to investors and regulators alike. This paper represents a new addition to existing factor pricing approaches since the risk variables of ESG are included directly in asset pricing models and are put through a climate stress-testing variety analysis. Precisely, the study incorporates climate-related financial variables like carbon intensity, ESG score and exposure to green bonds in the traditional Fama-French and Carhart models. The study bridges a considerable gap in academia and fieldwork. The present literature has studied the ESG factors qualitatively or has investigated their relationship with the firm-level performance, yet they have not incorporated or integrated such risk into traditional asset pricing models. Regulators, Concurrently, regulators including the European Central Bank and the Bank of England, are increasingly requiring climate stress tests, indicating that, compared to academia, the financial community is catching up in ensuring accounting with distinction. The paper aims to fill that gap, developing a basis in econometric theory of climate-aware asset pricing
Rewilding Capitalism: Designing Regenerative Entrepreneurial Models for Post-Growth Markets
This research is rooted in the concept of regenerative business models, post-growth economies and indigenous ecology. To a viable and just economic society, peace and safety to the soul must have a place as well. That’s why these are all linked. The emergence of new markets for goods and services. Sustainable alternatives aim to eliminate as many of the adverse impacts on the environment from business operations as they can. New products and services in the marketplace. But regenerative business models function at a level above sustainability, not in simple search of their opposite. They also try to make a difference in society and the environment for the betterment. Post-growth economies are refuting the objectives of maximizing profits by working for human well-being within environmental limits, and conventional ecological models have much to teach us about sustainable land management and ecological restoration over time scales. Post-growth economies depart from a profit-maximising system and are based on human well-being within ecological limits. And we have a lot to learn from indigenous ecological traditions about long-term land stewardship and ecosystem restoration, as well. This paper is a compilation of information found from literature, case studies and news sources in order to illustrate the potential such combined efforts have in terms of long-term regenerative solutions. Trinergy: Real-world Use Cases Examples, such as Attune food forest in Himachal Pradesh, India, demonstrate how principles of regenerative business models can play a part in the restoration of ecological systems they exist within and enable local communities to thrive. As emphasized in the study, regulatory support, co-construction and defining new recorded indicators for success need to be the minimum conditions if a real transition from Sustainable Changing to regenerative economies and eventually a post-growth economy will ever come. The findings underscore the need for more research, international collaboration and a holistic approach to sustainably secure the future.
The Role of FinTech in Enhancing Financial Inclusion in Emerging Economies
Financial technology, or FinTech, is helping to include more people in financial services in emerging countries because conventional banks usually miss these regions. Mobile gadgets and greater internet use have made it possible for fintech to serve the unbanked and underbanked with mobile money, digital wallets, and new credit scoring. Mobile banking and the use of blockchain allow for lower spending, more safety for users, and easier access to financial services for those on the outskirts of civilization. Platforms like M-Pesa have made it possible for millions in Africa and Asia to take part in transactions, manage their savings, and get loans all through their phones. Microloans are now reaching individuals and small businesses through artificial intelligence-supported lending methods, allowing them to get the support they need and giving them access to new markets. Experience from around the world suggests that fintech benefits developing countries more than advanced economies, mainly if the regulatory environment is well-established and there is a focus on digital education. Although these developments have taken place, there are still issues, for example, ensuring consumer protection, helping people become more digital, and ensuring everyone is treated fairly through appropriate regulations. As fintech grows, it will be important for fintech companies to join efforts with governments and banks to use digital technology for financial inclusion in emerging markets fully.
The Role of AI and Big Data in Predictive Financial Analytics and Investment Strategies
It is reshaping the financial industry through revolutionizing predictive analytics as well as investment strategies by the use of Artificial Intelligence (AI) and Big Data analytics. Traditionally, analytical techniques no longer suffice to process the huge volume, velocity and variety of financial data that are being generated daily. In this context, AI algorithms, particularly those that incorporate Machine Learning (ML), deep learning, and Natural Language Processing (NLP), are effective tools that enable the development of models for making real-time decisions and managing risk. This paper attempts to establish the convergence of AI and Big Data in the financial sector, especially in predictive financial analytics and investment strategies. They enable forecasting of accurate markets, assessing risk, optimization of portfolios, detecting fraud, and sentiment analysis. A review of Big Data platforms, including Hadoop and Spark, and methodological frameworks such as supervised learning, unsupervised learning, and reinforcement learning, is provided. The practical applications and limitations are illustrated through a comprehensive literature review, methodological analysis, and case study. A significant improvement in the accuracy of the forecast and investments' return presented in the results confirms the importance of applying AI and Big Data in modern financial infrastructures.
Predictive Analytics in Behavioral Finance: Modeling Investor Sentiment with NLP Techniques
Behavioral finance with predictive analytics is a developing discipline that tries to understand how psychological powers and behavioral predispositions impact the budgetary choices of investors. In this research paper, utilizing Natural Language Processing (NLP) techniques for modeling of the investor sentiment is studied, using text data from financial news, social media, blogs and financial statements. Sentiment of the investors (an important variable in behavioral finance) can cause significant volatility in the market as well as the pricing of the assets. Quantifying sentiment is complicated and subjective, though. In this paper, we present a flexible framework with sentiment analysis, topic modeling and deep learning, which explores the output of unstructured financial text data as Actionable Sentiment Scores. The research starts off by investigating the impact of behavioral finance and data science on financial analysis, with a focus on the way that non-rational factors, including emotions and biases, play a role in financial decision-making. It then goes on to explain recent advances in NLP, particularly in transformer-based models such as BERT and FinBERT and their applicability for finance-specific applications. Following a multi-source data aggregation strategy, this study extracts investor opinions and applies a sentiment classification, which is further utilized with a time series regression model to evaluate how sentiment trends affect stock price changes. However, findings from this paper also offer quantitative outcomes that show a statistically significant correlation when it comes to aggregated sentiment scores and abnormal returns in specific market segments. The experimental setup contains data from 2017 to 2023, whereas the key tools utilized are Python, HuggingFace Transformers and APIs related to finances. A hybrid model is used to model sentiment with rule-based approaches such as VADER, Loughran-McDonald lexicon and machine learning such as LSTM and BERT. This shows the added value to both theoretical and applied domains by combining behavioral theories of finance and the latest computational techniques to improve market prediction models and make better decisions. Implications for practice include the development of real-time sentiment dashboards for traders as well as risk managers. It finally concludes by suggesting future directions of research, like multilingual sentiment analysis and building real-time prediction engines.
Multi-Agent AI Systems in Business Strategy: A Computational Approach to Competitive Analysis
Multi-Agent AI Systems (MAS) rely on the cooperative actions of autonomous agents to meet difficult and rapidly changing issues in analysis and business strategy. In contrast to single-agent models, MAS includes different agents that team up, change as needed and function in real time. Thanks to its decentralized and modular design, businesses can scale their activities, maintain good stability and flex their operations as market situations change. With the help of advanced AI like Generative AI, MAS can examine huge datasets, perform market simulations and support smart decisions from leaders. Such algorithms are applied to everything from setting creative prices to improving supply chains, assessing risks and detecting fraud in the financial industry. The use of MAS makes it possible for tasks to be split and completed by multiple processors, which helps reduce workflow trouble spots. Additionally, its ability to respond to uncertainty and make quick, real-world decisions makes MAS a vital instrument for industries needing both agility and innovation. With MAS, organizations become stronger competitors by streamlining their work processes, encouraging innovation and solving problems on many scales. The future success of MAS comes from its power to change how businesses run smoothly by working with present technology and developing together with the company's needs.
AI-Driven Cybersecurity Strategies: Leveraging Machine Learning for Threat Detection and Risk Mitigation
Traditional techniques used for threat detection and threat management are not strong enough to cope with the fast-changing world of cyber threats. With the help of Artificial Intelligence (AI) and Machine Learning (ML), it is now possible to strengthen cybersecurity strategies. This document explains how AI and ML are being utilised in cybersecurity for both threat detection and risk mitigation. We look at the main theories, useful practices, and possible future trends in AI-powered cybersecurity. The document presents several case studies, algorithms, and statistical information to demonstrate how these technologies operate in the real world. Ultimately, we cover the obstacles and ethical topics relevant to AI in cybersecurity.