OPH-Guard: An Operationally Interpretable Tree-Ensemble Framework for Phishing URL Screening in Secure Web Access Management

Reem

doi:https://doi.org/10.54216/JCIM.180104

American Journal of Business and Operations Research | 2026

Data-Driven Capital Allocation in Manufacturing Firms: An Investment Analytics Study Using Public Panel Data

This paper evolves a business data analytics approach to capital allocation by exploring how the use of public panel data can aid in estimating, classifying, and profiling strategic firms. The paper examines the claim that lagged market value, capital stock, and growth signals can explain the current investment behavior and hint when the investment activity is unusually high using the public-domain Grunfeld investment data, which has annual observations of major U.S. manufacturing firms. The empirical design is deliberately non-standard as compared to typical forecasting research and it consists of three analytical layers; fixed-effects panel estimation, supervised classification of high-investment periods, and firm-level strategic segmentation. The findings indicate that the growth of lagged investment, lagged capital stock and firm value is highly correlated with the present level of investment, and that machine-learning classifiers offer helpful discrimination of high in-vestment periods. Strategic segmentation exercise also indicates the clear profiles of firms that can be used to prioritize resources and track capital. The value of the paper is two-fold. First, it illustrates how an old, conventional, public data may be re-used as a new business data analytics example to support decision-making. Second, it interprets quantitative results into a managerial advice on capital planning, growth monitoring, and portfolio-style firm evaluation. Accordingly, the paper provides a reproducible submission-ready study that has a different structure than the traditional business intelligence forecasting papers and is more in line with the requirements of strategic financial analysis and data-driven capital allocation.

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Syed Muhammad Mudassir Abbas Naqvi mail -

Ahmed Usman mail

link https://doi.org/10.54216/AJBOR.140202

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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American Journal of Business and Operations Research | 2026

A Business Intelligence Framework for Short-Term Consumer Demand Forecasting Using Public Macroeconomic Indicators

Business intelligence has emerged to be a high-level managerial competency among organizations that aim to enhance the quality of planning, responsiveness in operations and evidence-based decision making in uncertain market environments. Short-term demand forecasting is one of its most important business applications since fluctuations in demand expectations affect budgeting, inventory planning, staffing, procurement timing and revenue management. The paper formulates and tests a business intelligence system of consumer demand prediction over short-term with the help of the public macroeconomic variables. It aims to show how external economic signals may be converted into an explainable, reproducible, and useful forecasting layer to be used in dashboards and decision support systems. The research forecasts next-period real consumer spending using lagged indicators based on output, disposable income, investment, unemployment, inflation, and short-term interest rates using a publicly available U.S. macroeconomic data, which is periodically updated. Ordinary least squares, ridge regression, random forest and gradient boosting are compared by using a chronological holdout design. The empirical findings indicate that the regression-based models that are interpretable have the best out-of-sample performance, and ordinary least squares model has the lowest error and greatest explanatory power. The results suggest that effective business forecasting support can be offered using transparent analytics without the need to use complex black-box models. The study is valuable because it adds to the body of business intelligence literature a reproducible external-signal prediction pipeline, a comparison of the explainable and non-explainable models in a management context, and a translation of the forecasting results into operational and strategic planning consequences.

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Ilhan Ozturk mail

link https://doi.org/10.54216/AJBOR.140203

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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American Journal of Business and Operations Research | 2026

Business Data Analytics for GCC Travel and Tourism SMEs Under Geopolitical Disruption

The paper creates the business data analytics vision of how small and medium-sized enterprises (SMEs) of GCC travel and tourism ecosystems can mitigate the commercial disruption as the perceived cost, uncertainty, or inconvenience of air travel increases due to geopolitical friction in the region. Since there is a lack of public GCC micro-level booking and itinerary data, the research paper relies on a similar public dataset: the travel mode-choice dataset published under the name of statsmodels and initially based on the intercity mode-choice literature. The benchmark is operationalized as an analogue of disruption-sensitive travel demand reallocation and poses a managerial question, not a simply transport question: in the event of a shock that increases generalized cost and waiting frictions on the most exposed mode what are the most likely demand reallocations and how should SMEs respond? Empirical design transforms the data in the long-format alternative-choice form into an analytical platform that is business-facing and integrates multinomial logit, random forest, gradient boosting, and scenario stress testing into a single analytical framework. The findings indicate that the random forest model provides the best out of sample predictive performance (accuracy 0.981; macro-F1 0.973), whereas the multinomial logit model is useful in translating scenarios that can be understood. Average predicted air share decreases by 28.0 to 16.1 percent with simulated air-travel disruption, and train-like substitutes acquire most of the share. The results suggest that GCC travel, hospitality, and mobility SMEs cannot afford to trust one open channel when a period of geopolitical escalation occurs, but rather they should develop substitution-ready packages, flexible repricing guidelines, and portfolios of partnering that encompass low-friction options. The article adds a unique business analytics template of demand reallocation sensitive to crisis through the use of repeatable public information and underlines practical resilience solutions as opposed to self-forecasting wars.

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Shummaila Afzal mail -

Sidra Sohail mail -

Sana Ullah mail

link https://doi.org/10.54216/AJBOR.140204

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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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

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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

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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

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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.

groups

Saad Metawea mail -

Maha Metawea mail

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

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of International Economics Research | 2026

Sport tourism as a Driver of Soft Power and Regional Growth

The research shows the currently increasing role of the industry of sport tourism in the Republic of Uzbekistan and how it impacts on the economic and social development of the state from 2018 to 2025. Based on secondary data from international and national organizations and analytical think centres, the paper analyses trends in tourist arrivals, tourism revenue and the impact of government reforms: visa liberalisation and investment in sport facilities. The results of this study shows that sport tourism is a crucial factor in economic diversification, aiding job creation, regional development and the growth of small and medium businesses. The different international competitions such as boxing or judo championships raise the country's status globally and also contribute to strengthening its strength and cultural interaction. Because of different reforms and because more people are now interested in active travel, tourism in Uzbekistan has recovered after the pandemic. However, scientists still have not studied enough how sport affects the economy of the country and small businesses. Overall, the results indicate that sport tourism is very important for the development of Uzbekistan. It can help the country to reach its long term goals and become one of the best places for active and sport tourism in Central Asia.

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Khodjaeva Dildora mail -

Khaydarova Marjona mail

link https://doi.org/10.54216/JIER.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Price-Aware and Explainable Analytics for Urban Electric Vehicle Charging Networks: Forecasting Utilization Regimes for Sustainable Charging Operations

The efficient functioning of the electric-vehicle charging systems that are publicly operated has become focused on reliable short-horizon forecasting. The paper establishes an explainable and price-conscious analytical model to predict short-term charging usage and demonstrate the utility of tariff signals in an urban charging system. The analysis is based on UrbanEV benchmark, a new six months hourly panel of Shenzhen public charging infrastructure, which integrates occupancy, charging time, charging volume, electricity tariffs, service charges, weather and spatial descriptors. The concept of charging occupancy is considered an operation state variable with connection to queue exposure, reliability of service, and tactical intervention. A succinct mathematical formulation is created to use it in one-step-ahead utilization forecasting and in interpreting low-, medium-, and high-utilization regime. The empirical analysis is pegged to benchmark evidence reported to UrbanEV, where transformer-based forecasting had the best node-level performance and TimeXer had the best RMSE values of 0.07 in occupancy, 2.73 in charging duration, and 43.66 in charging volume. Further discussion indicates that occupancy prediction is accurate enough to justify regime based intervention and strongest additional gains are obtained through the joint effect of pricing variables and temperature-price interactions as opposed to single covariates. The results justify the justifiable, price-conscious forecasting as an operational decision tool to alleviate congestion, design tariffs and specific capacity planning in sustainable charging networks.

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Heba Moselhy mail -

Noura Metawa mail

link https://doi.org/10.54216/JSDGT.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2026

OPH-Guard: An Operationally Interpretable Tree-Ensemble Framework for Phishing URL Screening in Secure Web Access Management

Phishing URLs still present a security threat to organizations because they enable credential theft and account takeover together with payment fraud and unauthorized digital service access. The existing research on phishing detection has been studied extensively yet most published papers still show a preference for predictive performance assessment compared to operational system capabilities and tests and governance system implementation. The researchers developed OPH-Guard as an operational security system which uses compact tree ensembles to identify phishing URLs for their secure web access management system. The integrated workflow system enables institutional and small enterprise to implement public data ingestion and feature validation together with tabular model learning and post-hoc explanation and security-action mapping. The empirical evaluation used a public GitHub-hosted phishing URL dataset which contains 11,481 labeled records and 87 predictive features. The researchers conducted a comparison between three tree-based learners according to a stratified 80/20 hold-out protocol which included Decision Tree and Random Forest and Extra Trees. The actual results from Extra Trees produced the highest accuracy score of 0.9856 which included 0.9921 precision and 0.9791 recall and 0.9855 F1-score and 0.9984 ROC-AUC from the held-out test results. The study investigates security relevance for top predictors through google index and page rank and domain age and phish hints which provide evidence that the resulting model enables organizations to manage browsing risk through URL triage together with secure information management controls. The study presents a reproducible framework together with a complete screening algorithm and a summary of existing research from ten studies and a system which connects model results to security operations.

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Reem Atassi mail

link https://doi.org/10.54216/JCIM.180104

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new