Amer Morshed

This study examines the impact of operating capital management (WCM) strategies on the monetary execution of small and medium-sized enterprises (SMEs) in Poland, with a particular focus on finding the correct equalization between liquidity and benefit. The review utilizes relapse investigation to survey the effect of forceful and conservative (WCM) techniques on the benefit and fluidity of 4,891 Polish SMEs from 2012 to 2021, as measured by an informational index of budgetary and operational information. The results demonstrate a noteworthy connection between WCM improvements and budgetary results. However, aggressive actions do not just mean higher earnings; they also involve heavier financial risks. On the other hand, cautious methods are linked with stronger financial stability but may lead to lower profit. According to the survey, when cash conversion cycle (CCC) days fall by 1%, return on total assets (ROA) can increase by approximately 1:0 percentage points. This demonstrates again that WCM is very important in improving company profits. These findings have implications for academics, practitioners, and government officials.

Acceptance and implementation of international financial reporting standards ensure a wider scope for financial transparency, accountability, and comparability on a global scale. Against this backdrop, this study looks at the implications of these standards on Hungary’s financial transparency by evaluating panel data from 716 private companies over the period 2013–2023. The Hausman test results suggest that Fixed and Random Effects models should be used.
The analysis indicates that, on average, the sampled companies have improved financial transparency by 75%. Key determinants include standard adoption (0.025 coefficient, t = 8.333, p < 0.001), cost of implementation (2.400 coefficient, t = 24.000, p < 0.001), investor confidence (0.035 coefficient, t = 11.667, p < 0.001), and legislative changes (2.450 coefficient, t = 24.500, p < 0.001). Moreover, it is possible to obtain significant positive effects on the centered variables for implementation costs (coefficient = 2.498, p < 0.001) and government efficiency (coefficient = 0.036, p < 0.001).
These results demonstrate a positive effect, which is significantly created by adopting these standards on financial transparency. They underline increased investor confidence and government efficiency as drivers of these improvements. Applying these standards in Hungary’s financial reporting system is classified as a strategic tool to foster economic stability and attract foreign investment, which ensures Hungary’s good standing in the global economy.

Amid rapid technological advancements, the telecommunications sector in the United Arab Emirates increasingly adopts big data analytics to optimize customer relationship management. This study investigates the effects of big data on customer satisfaction, decision-making, operational efficiency, and ethical practices. Data from 296 stakeholders, including employees, management, and customers, were analyzed using structural equation modeling with the Analysis of Moment Structures.
The results demonstrate a strong positive correlation between big data integration and improved decision-making in customer relationship management (r = 0.75, p < 0.001), which significantly enhances customer satisfaction (r = 0.80, p < 0.001). Additionally, big data integration directly influences customer satisfaction (r = 0.42, p < 0.001), further validating its critical role. However, ethical data usage presents challenges, showing a negative correlation with customer satisfaction (r = –0.15, p < 0.05) and decision-making (r = –0.50, p < 0.001). Descriptive statistics indicate strong approval for big data integration (mean = 3.6) and decision-making (mean = 3.93), while ethical practices score lower (mean = 3.38), and the complexity of big data analytics remains high (mean = 4.43), revealing significant implementation barriers.

Adopting International Financial Reporting 9 is critically relevant as it significantly transforms accounting practices, particularly in credit risk management, for banks in Jordan. The primary purpose of this study is to examine the impact of implementing International Financial Reporting 9 on the financial performance and credit risk management practices of Jordanian banks. A quantitative analysis was conducted using the Difference-in-Differences approach and Fixed Effects models on data from 19 banks operating between 2012 and 2022.
The results indicate that the adoption of International Financial Reporting 9 led to a substantial increase in loan loss provisions, with a mean increase of 0.25 (t-value = 18.00). This increase in loan loss provisions negatively affected profitability metrics such as Return on Assets and Return on Equity, which showed mean decreases of 0.0857 (t-value = 4.22) post-implementation. Despite the negative impact on profitability, the findings also highlight improvements in financial transparency and stability due to more accurate credit risk assessment.
While the adoption of International Financial Reporting 9 imposes operational and financial challenges, it enhances the robustness and clarity of financial reporting in Jordanian banks.

Type of the article: Research Article

Abstract
The emergence of Islamic finance has positioned Sukuk as a moral substitute for traditional bonds. However, misalignment with International Financial Reporting Standard 9, especially in Jordan, erodes investor confidence and reduces integration into world markets. This paper attempts to quantitatively evaluate how classification difficulties under International Financial Reporting Standard 9 affect investment strategies, decision-making, and market attractiveness of Sukuk within Jordan’s financial system.
Data were collected from a stratified sample of 346 finance professionals from banks, investment businesses, insurance companies, and regulatory authorities. Each participant had at least three years of work experience and suitable academic credentials. Utilizing partial least squares structural equation modeling, the survey was carried out between September 2024 and January 2025. The results indicate that classification issues have a significant adverse effect, reducing investment strategy efficacy by 46% (β = –0.46, p < 0.01), decision-making clarity by 37% (β = –0.37, p < 0.05), and Sukuk attractiveness by 52% (β = –0.52, p < 0.001). These significant effects are reinforced by vigorous diagnostics of the model, with variance inflation factor measures between 1.15 and 1.23, and by superb fit indices of the model, such as a standardized root mean square residual of 0.06 and a comparative fit index of 0.95.
The results underline the need for a coordinated international classification system and the structural influence of regulatory inconsistencies on Sukuk viability. Promoting openness, restoring investor confidence, and enabling wider acceptance in foreign markets all depend on aligning Islamic financial instruments with global reporting standards.

Type of the article: Research Article

Abstract
The increasing complexity of insurance fraud in Jordan has unveiled inadequacies of traditional detection mechanisms, calling for advanced technologies. This study investigates drivers and inhibitors of machine learning adoption for fraud detection within Jordan’s insurance sector, with a focus on institutional readiness, ethical concerns, and supporting regulations. By applying quantitative and exploratory research design, Partial Least Squares Structural Equation Modeling serves as an approach to analyze data collected from 291 practitioners of fraud detection, data science, and insurance compliance in the industry.
Findings show that both existing fraud detection efforts (coefficient = 0.42, p = 0.012) and knowledge of machine learning (coefficient = 0.55, p = 0.009) have favorable impacts on adoption likelihood, which underlines the relevance of bureau experience and informed professional culture. By contrast, major adoption deterrents such as limited IT capability, budgetary constraints, and moral concerns about fairness and clarity (coefficient = –0.40 and –0.38, respectively) unfavorably decrease adoption intention.
Regulatory encouragement has a two-fold role: it has a direct promoting effect on adoption (coefficient = 0.47, p = 0.011) and a buffering effect on negative ethical concerns (interaction = 0.36, p = 0.025) and adoption barriers (interaction = –0.28, p = 0.032). Perceived efficacy also mediates between awareness/experience on the one hand and adoption decisions on the other (coefficients = 0.51 and 0.44, p < 0.05).
The results demonstrate successful incorporation of machine learning into fraud detection as depending on the clarity of regulations, ethical protections, and institutional readiness, rather than on technical capability itself.