Active Agent Capacity and Liquidity Discipline in Mobile Money Operations: A Business Analytics Perspective

Heba; Dina K.

doi:https://doi.org/10.54216/FinTech-I.040102

Journal of International Economics Research | 2026

How Education and Investment Affect Economic Growth in Asian Developing Countries?

This study investigates how education and investment influence economic growth across selected Asian developing countries, with a particular focus on whether these relationships differ by income level and institutional context. Using panel data from 2010 to 2023, countries are divided into upper-middle-income (China, Indonesia, Malaysia, and Kazakhstan) and lower-middle-income (Uzbekistan, Tajikistan, Pakistan, Kyrgyzstan, Bangladesh, Philippines) groups. The analysis employs multiple regression models to examine the effects of government education expenditure, school enrollment rate, gross capital formation, labor force participation, foreign direct investment, and population dynamics on GDP per capita. Diagnostic tests, including Variance Inflation Factor (VIF), are applied to ensure model reliability. The findings reveal that education plays a stronger role in promoting economic growth in upper-middle-income economies, while lower-middle-income countries experience weaker and less consistent relationships. The findings suggest that the growth effects of education and investment depend on development level and institutional quality, emphasizing the need for tailored policy approaches. In upper-middle-income countries, policies should prioritize improving education quality and aligning skills with labor market demands, while also strengthening the connection between investment and workforce capacity. In contrast, lower-middle-income countries require broader structural reforms, including better governance, improved education outcomes, and incentives for school completion.

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Malika Rajabova mail -

Nurdaulet Karabayev mail

link https://doi.org/10.54216/JIER.040204

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of International Economics Research | 2026

Formation of a System of Regional Indicators for Assessing Economic Security: Methodology, Validation, and Empirical Application

The formation of a coherent, statistically robust, and policy-relevant system of regional indicators for economic security assessment represents one of the most complex methodological challenges in contemporary regional science. This paper presents an original, multi-stage methodological framework for constructing such a system, grounding it in a synthesis of international practice, economic theory, and rigorous empirical validation. The proposed Regional Economic Security Index (RESI) integrates 30 indicators organized across 10 analytical dimensions all drawn from internationally standardized, publicly available data sources. The framework employs a two-tier aggregation architecture, combining weighted arithmetic and geometric means at the sub-index level before computing the overall composite score. Methodological robustness is demonstrated through Cronbach’s alpha reliability testing (α = 0.847), Kaiser-Meyer-Olkin adequacy assessment (KMO = 0.812), confirmatory factor analysis, Monte Carlo sensitivity simulation across 10,000 weight perturbation scenarios, and Delphi-based expert validation involving 34 specialists from 12 countries. The index is piloted across 10 illustrative regional cases spanning Europe, Central Asia, and Latin America, revealing substantial cross-regional heterogeneity and validating the framework’s discriminatory power. The study identifies seven archetypes of regional economic security vulnerability and recommends archetype-specific indicator prioritization strategies. The findings have direct implications for national statistical agencies, regional development authorities, and international organizations seeking to move beyond GDP-centric assessments toward multidimensional, early-warning-capable monitoring systems. The paper concludes with a replication protocol and open data framework to facilitate adoption in data-limited environments.

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Anvar Islamov mail

link https://doi.org/10.54216/JIER.040205

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2026

How Should Higher Education respond when AI enters the Classroom?

Driven by the global informatization wave, AI technology is leading a profound change in education, and the traditional higher education model is facing unprecedented opportunities and challenges. Firstly, the advantages of the comprehensive integration of “AI+ Education” are sorted out from the two aspects of students and teachers. Secondly, it analyzes the transformation of teachers from classroom manager to learning ecological designer from three aspects: education personalization and fairness, man-machine teaching collaboration and interdisciplinary scene innovation. Next, the construction of student evaluation system is discussed from four dimensions of thinking visualization. Finally, the author expounds the characteristics of the teaching profession that cannot be replaced by AI.

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Wenxin Yu mail -

Yuan Huang mail

link https://doi.org/10.54216/IJAIET.050205

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare

Rapid growth in the adoption of Electronic Health Records (EHRs), Internet of Medical Things (IoMT) devices, wearable sensor technology, and digital healthcare systems offers immense scope for intelligent healthcare decision support. However, most AI-enabled healthcare systems in use today still lack explainability, contextual reasoning capabilities, and effective decision-making. For these reasons, this research develops an Agentic Generative AI Framework for Intelligent Disease Prediction and Decision-Making in smart healthcare. The framework incorporates predictive analytics, Generative AI-based clinical reasoning, and autonomous intelligent agents into a coherent healthcare framework. Six specific agents are used for data gathering, data analysis, disease prediction, clinical reasoning, treatment recommendations, and patient monitoring. The combined functionality of these agents supports disease prediction, clinical reasoning, and personalized treatment plans. Evaluation was performed on healthcare datasets related to heart disease, diabetes, chronic kidney disease, and breast cancer. Experimental results show high efficiency, stable accuracy across diseases, reliable recommendation generation, and enhanced healthcare intelligence compared with traditional ML, DL, and LLM methods. Results show that combining Agentic AI with Generative AI increases explainability, adaptability, and efficiency in medical decision support. The proposed model represents an encouraging path toward intelligent, patient-centered, and explainable smart healthcare systems.

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S. Phani Praveen mail -

Massila Kamalrudin mail -

Sai Vellela mail -

Deshinta Arrova Dewi mail -

Dedeepya Pulletikurthy mail -

Vahiduddin Shariff mail

link https://doi.org/10.54216/IJAACI.080105

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Business Analytics for Green Electricity Transition Planning: Explainable Forecasting of Renewable Electricity Shares from Cross-Country Energy Indicators

Renewable electricity growth is central to sustainable development, decarbonization, and green-technology planning. However, much of the forecasting literature remains focused on plant-level or narrow-horizon technical prediction, with limited attention to country-level decision support for investment screening, transition monitoring, and strategic benchmarking. This study develops a business analytics framework to forecast the renewable share of electricity generation and classify countries by renewable-transition level using a cross-country panel based on the Our World in Data Energy database. The empirical sample comprises 5,162 country-year observations from 213 countries over the period 2000–2025 and includes measures of electricity demand, electricity generation, primary energy use, greenhouse-gas emissions, and energy-system structure. Three regression models and three classification models were evaluated using a fixed train–test de sign. The random-forest regressor achieved the best continuous forecasting performance, with MAE = 3.536, RMSE = 6.466, and R2 = 0.960, while the random-forest classifier delivered the best tier-classification performance, with 93.998% accuracy and macro-F1 = 0.940. Feature-importance analysis identified greenhouse-gas emissions, energy intensity, electricity generation, electricity demand, and per-capita electricity consumption as the most influential predictors. The findings indicate that renewable-transition benchmarking can be framed as a managerial analytics problem, extending sustainability research beyond descriptive monitoring toward practical decision support for business and policy planning.

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Saad Metawea mail -

Maha Metawea mail

link https://doi.org/10.54216/JSDGT.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Assessing the Need for an MBA in Sustainable Economics and Finance: Evidence from Selected Higher Education Contexts in Uzbekistan

This study assesses whether there is sufficient justification for developing an MBA in Sustainable Economics and Finance in selected higher education contexts in Uzbekistan. The research is based on two surveys conducted among stakeholders and prospective candidates. The findings show that sustainability-related economic and financial competencies are viewed as increasingly relevant, while stakeholders also identify clear skills gaps in this area. Prospective candidates show a generally positive but still conditional interest in the proposed programme. The results suggest that the programme would be most viable if designed as a practical and career-oriented MBA with strong emphasis on applied skills, internships, and real-world relevance. At the same time, affordability, language accessibility, and expected career outcomes appear to be important conditions shaping demand. Overall, the study concludes that the proposed MBA has a credible foundation in the surveyed context, but its success will depend on careful programme design and alignment with labour-market needs.

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Gavkhar Isamutdinova mail -

Ugilshod Akhmedova mail

link https://doi.org/10.54216/JSDGT.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of International Economics Research | 2026

The Evolution of the Middle Corridor and Its Implications for Kazakhstan’s International Trade in the Aftermath of the Russia–Ukraine War

After the Russian invasion of Ukraine in 2022, the role of Middle Corridor raised and this paper examines Kazakhstan’s trade turnover with ten partner countries that reflect the development prospects of the Middle Corridor. Using graphical methods, economic-statistical analysis, as well as explanatory analysis, the study identifies key factors influencing the development of the Middle Corridor. The research is based on data from the Bureau of National Statistics on the website of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan on investments and trade turnover from 2021 to 2025. This research argues that the growing interest in the Middle Corridor has influenced Kazakhstan’s economic trajectory, which, in turn, is shaping the country’s multi-vector foreign policy.

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Aidarov Tofik Aga-Balaevich mail -

Yusubaxmedova Durdonaxon mail -

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/JIER.040201

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

Valuation Premium Analytics in Global Public Companies: A Cross-Sectional Study Using 2024 Public Fundamentals

This paper explores why there are listed companies that are valuing significantly higher in the market based on their asset base compared to other companies. It analyses the relationship between valuation premiums and profitability, asset efficiency, the combination of the two, the size of the firm and its loss status using a cross-section of the largest publicly traded companies in the world in 2024. The empirical design integrates the predictive analytics and hypothesis testing. During the explanatory phase, a strong ordinary least squares specification is used to model the logarithm of the market value divided by the total assets. In the predictive stage, logistic regression, random forest, and gradient boosting are used to identify firms in the top quartile of the valuation-premium distribution. The findings show that profitability and asset efficiency interaction is the most positive correlate of the valuation premium, and firm scale is the most negative correlate of relative valuation after standardization by assets. The interaction-enriched specification enhances explanatory power with significant material in comparison to an interaction-free model. The discriminatory performance of the tree-based models tends to be high in the classification phase, with random forest performing out of sample with an AUC of more than 0.93. The results of these studies indicate that valuation premium should be viewed as a combined operating-quality indicator and not as a reward to margin performance in isolation and can serve as a useful guide to screen a portfolio, benchmark a company and interpret market multiples.

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Saad Metawea mail -

Maha Metawea mail

link https://doi.org/10.54216/AJBOR.140205

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Financial Technology and Innovation | 2024

Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)

The application of supervised machine learning (ML) algorithms for equity market regime classification has gained significant attention in recent years. This systematic literature review (SLR) synthesizes findings from 16 peerreviewed studies published between 2015 and 2024 to address three research questions: (1) How do supervised ML algorithms (XGBoost, Random Forest, SVM, Neural Networks, Ensemble methods) compare in accuracy, robustness, and computational efficiency for market regime classification? (2) What feature engineering approaches are most effective? (3) How generalizable are these models across different equity markets and time periods? Following PRISMA 2020 guidelines, we searched IEEE Xplore, ScienceDirect, and Springer, identifying 2953 records and including 16 studies after screening. Our findings indicate that ensemble methods (particularly Random Forest and XGBoost) and deep learning approaches (LSTM, DNN) consistently outperform traditional classifiers. Technical indicators remain the most common features, though novel approaches including event embeddings, network centrality measures, and signal decomposition show promise. Generalizability remains a challenge, with most studies focusing on developed markets. We identify gaps in cross-market validation and interpretability, providing directions for future research.

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Suvonkulov Abdulaziz mail -

Eugene Q. Castro mail

link https://doi.org/10.54216/FinTech-I.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2024

Active Agent Capacity and Liquidity Discipline in Mobile Money Operations: A Business Analytics Perspective

Mobile money has evolved into a business-critical financial technology infrastructure, yet its operating strength cannot be judged from customer scale alone. A platform may report rapid growth in registered accounts, transaction value, or agent coverage while still facing service fragility when active agents do not expand at the same pace as transaction demand. This study develops a business analytics model for evaluating active agent capacity, customer activation, transaction intensity, and liquidity pressure as connected dimensions of mobile money operations. The empirical analysis uses public aggregate indicators from mobile money industry reporting and demand-side financial inclusion indicators from the Global Findex database. The model distinguishes between three managerial questions that are often combined in practice: whether customers are becoming active users, whether agents are becoming productive service points, and whether transaction value places increasing pressure on the active agent base. The results show that transaction value and transaction volume grow more rapidly than customer scale, while registered agent expansion exceeds active agent growth. Scenario analysis indicates that agent reactivation can reduce liquidity pressure, whereas customer activation without corresponding service-capacity expansion increases operational stress. The study contributes a practical measurement lens for FinTech managers, payment providers, investors, and regulators seeking to scale mobile money while maintaining reliable last-mile service capacity.

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Heba Moselhy mail -

Dina K. Hassan mail

link https://doi.org/10.54216/FinTech-I.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new