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 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.

Type of the article: Research Article

Abstract
Growing regulatory demands and operational complexity in the insurance industry require professionals with strong analytical reasoning and professional skepticism, yet traditional training often fails to develop these competencies. This study aims to evaluate the effectiveness of simulation-based experiential training in enhancing professional skepticism and analytical reasoning among insurance professionals and to examine how the organizational learning climate influences this relationship.
A quasi-experimental study was conducted between May and September 2025 across eight insurance companies in the United Arab Emirates, involving 160 early-career professionals (mean age = 27.3 years, SD = 2.5) organized into 40 teams. Teams were randomly assigned to either simulation-based experiential training or conventional instruction. Data were collected at three stages – pre-training, post-training, and eight weeks after training – and analyzed using multilevel structural equation modeling.
Participants who received simulation-based training showed a 0.42-point increase in professional skepticism and a 0.78-point improvement in analytical reasoning compared with the control group, both statistically significant at p < 0.001. Analytical reasoning mediated 57% of the training’s total effect on skepticism (indirect effect = 0.24, p < 0.001). The organizational learning climate significantly moderated this relationship (interaction effect = 0.21, p < 0.001), with greater gains observed in firms that promoted reflection and feedback.
The findings confirm that simulation-based experiential learning, reinforced by a supportive organizational climate, substantially enhances analytical, skeptical, and ethical judgment essential for accurate claim evaluation, risk assessment, and fraud prevention in the insurance sector.

Type of the article: Research Article

Abstract
Rapid digitalization is redefining how consumers evaluate Islamic banks, where technological progress must align with Shariah principles to ensure transparency, fairness, and credibility. In this context, digital marketing serves as a critical bridge between technological innovation and ethical communication. This study investigates how digital marketing effectiveness shapes trust and engagement, and how these factors, in turn, influence adoption intention in Islamic banking. It further examines the moderating role of religiosity and compares structural relationships across Saudi Arabia, the United Arab Emirates, and Jordan. A quantitative, cross-sectional survey conducted from January to April 2025 collected data from 824 clients of Islamic banks (Saudi Arabia = 297, United Arab Emirates = 205, Jordan = 322). The data were analyzed using partial least squares structural equation modeling, measurement invariance testing, multi-group analysis, and moderation-mediation procedures. All respondents were Muslim account holders who had interacted with an Islamic bank’s digital marketing campaign within the preceding six months. Digital marketing effectiveness significantly increased trust (β = 0.662, t = 15.42) and engagement (β = 0.628, t = 13.88). Adoption intention was jointly predicted by trust (β = 0.422, t = 10.17) and engagement (β = 0.377, t = 9.83), explaining 60.2 percent of the variance. Religiosity strengthened both relationships, with stronger effects in Saudi Arabia and the United Arab Emirates than in Jordan. Transparent, interactive, and ethically consistent digital marketing enhances trust and engagement, providing the behavioral foundation for Islamic digital banking adoption.

Type of the article: Research Article

Abstract
Financial fraud in Arab Gulf family firms threatens key stakeholders, yet many still rely on informal, trust-based controls. The purpose of this study is to explain why some family firms in this region adopt machine learning-based financial fraud detection systems, while others remain locked into legacy mechanisms of control. Results from a cross-sectional survey of 416 owners, executives, finance, and compliance officers from family businesses in Saudi Arabia, the United Arab Emirates, and Qatar were analyzed using variance-based structural equation modeling and multi-group analysis. The results show that perceived effectiveness (β = 0.08, p < 0.01) and regulatory support (β = 0.07, p < 0.05) are the strongest positive drivers of the likelihood of adopting machine learning-based fraud detection. Awareness of machine learning, ethical concern, and existing fraud detection practices also have significant positive effects (β ≈ 0.03-0.05, p < 0.05), while barriers to adoption exert a significant negative influence (β = −0.05, p < 0.05). The structural model explains 52.7% of the variance in the likelihood of adoption and 46.8% of the variance in perceived effectiveness. Indirect effects indicate that awareness and regulation promote adoption through perceived effectiveness, whereas barriers reduce adoption through heightened ethical concern. The findings suggest that stronger governance, clearer and incentive-aligned regulation, and explainable, well-governed machine learning implementations are essential to shift family businesses in the Arab Gulf region from trust-based to data-driven fraud detection.

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 integration of artificial intelligence-driven predictive analytics has redefined financial management and decision-making across Gulf economies. This study compares the performance of artificial-intelligence-based and traditional predictive models using data from twenty financial institutions from six Gulf Cooperation Council countries. A quantitative cross-sectional design was adopted, and analysis of variance revealed statistically significant differences (p < 0.001) across all indicators. Predictive accuracy increased from 83.5 to 91.5 per cent (F = 4.23 × 10²⁹), operational efficiency from 12 to 19.5 per cent (F = 1.31 × 10³¹), risk-management effectiveness from 7.0 to 9.3 points (F = 2.69 × 10³⁰), and customer satisfaction from 6.5 to 8.5 points (F = 1.69 × 10³⁰). Regression analyses confirmed these outcomes: model type produced significant coefficients for predictive accuracy (β = 8.21, p < 0.001), operational efficiency (β = 7.46, p < 0.001), risk-management effectiveness (β = 2.29, p < 0.001), and customer satisfaction (β = 1.84, p < 0.001). The overall model explained 84 per cent (R² = 0.84) of the variation in institutional performance, confirming the strong predictive power of artificial-intelligence models. These results demonstrate that intelligent predictive systems significantly enhance accuracy, efficiency, and stakeholder value. The study concludes that transparent and ethically governed analytical frameworks are essential for sustainable financial competitiveness and responsible innovation in the Gulf region.

Type of the article: Research Article

Abstract
Aligning environmental objectives with economic performance is an ongoing challenge in the renewable energy transition, especially in emerging solar markets facing operational inefficiencies and uneven policy implementation. This paper examines the environmental and economic efficiency of large-scale solar energy conversion projects by employing a Material Flow Cost Accounting methodology based on ISO 14051 standards. Based on the operating and financial performance data of 83 solar energy conversion projects (46 from India and 37 from Jordan) covering the period 2017–2023, avoidable energy loss costs due to dust settling, heat stress, grid curtailment, and plant downtime have been estimated and quantified cumulatively in both settings. The findings indicate a technical efficiency of 88.4% and 77.9% with an average energy loss potential of 128 and 274 gigawatt-hours per year in both settings of Jordan and India, respectively, causing an economic loss potential of 9.2% and 18.7%, respectively, collectively amounting to a financial loss potential of about 54.6 million annually. Systematic plant maintenance and coordinated use in electric grids increased output potential by about 16% and lowered costs by 13%, with efficient management options collectively leading to an 11.5% increase in financial returns in Jordan and a 19.3% boost in India’s financial performance. Based on findings, MFCA methodology is indeed capable of interlinking environmental protection with economic performance for efficient, sustainable energy policymaking within developing nations.