Abdulhadi Ramadan

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.

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
Corporate real estate financing is a channel through which macro-financial volatility, regulation, and strategic orientation affect firms’ balance sheets. This study explains how firms in the United Arab Emirates, Saudi Arabia, and Singapore choose between leasing, owning, and hybrid property-financing structures and how these choices perform under uncertainty. The empirical framework combines Generalized Structural Equation Modeling with Monte Carlo simulation using macroeconomic and real estate data, latent constructs for strategic orientation, financial constraints, regulatory pressure, and perceived risk, and an outcome indicating the dominant property-financing structure. Measurement reliability is acceptable (Cronbach’s alpha 0.77–0.82, composite reliability 0.83–0.87, average variance extracted 0.57–0.62). Structural estimates show that strategic orientation (β = 0.36) and financial constraints (β = 0.41) have significant effects on property-financing choices, and regulatory pressure also contributes (β = 0.27), and perceived risk reduces the likelihood of ownership (β = −0.38) while mediating strategic and regulatory influences (indirect β = −0.13 and β = −0.17). Country context significantly moderates the impact of financial constraints (β = 0.12) and perceived risk (β = −0.10). Simulation results indicate net present values of 3.75, 2.80, and 4.10 million USD for the United Arab Emirates, Saudi Arabia, and Singapore. The study concludes that property-financing structure is a strategic decision and that the combined structural-simulation framework is a useful tool for analyzing corporate decisions in heterogeneous markets.

Type of the article: Research Article

Abstract
Artificial intelligence is diffusing across Gulf Cooperation Council insurance markets, yet disclosure-based evidence remains fragmented on whether adoption is associated with organizational change or localized automation. This study examines a purposive disclosure-based sample of 120 insurers from Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, Oman, and Bahrain. Because inclusion required sufficient disclosure of artificial intelligence practices, the sample is not intended to represent the insurance market. The study examines whether disclosed artificial intelligence adoption is associated with organizational change through financial transparency and operational efficiency. The dataset is constructed from annual reports, audited financial statements, governance reports, environmental, social, and governance reports, investor materials, and regulatory documents. Documents from 2017 to 2023 are treated as an observation window, coded at the item level, and aggregated into one firm-level score per insurer for cross-sectional structural equation modeling. Results indicate positive associations from artificial intelligence adoption to financial transparency (β = 0.52, p < 0.001) and operational efficiency (β = 0.49, p < 0.001). Financial transparency (β = 0.41, p = 0.003) and operational efficiency (β = 0.38, p = 0.012) are associated with organizational change. The indirect paths through transparency and efficiency are statistically distinguishable from zero within the model. Because all variables are derived from similar disclosure evidence, the pattern is interpreted as disclosure co-patterning rather than proof of a mechanism. The findings are associational, not causal, representative, or longitudinal.

Type of the article: Research Article

Abstract
Municipal waste-to-energy is often assessed through technical efficiency, energy output, or regulatory compliance. Yet, these indicators do not show whether plants create positive social welfare after operating costs and environmental damages are monetized. This study examines how operational performance, emissions intensity, and stricter environmental valuation shape the net social benefit of German municipal waste-to-energy plants from 2017 to 2023. Germany serves as a benchmark case because its mature waste-to-energy sector, European emissions regulation, and public environmental and energy-market reporting enable transparent public-record welfare assessment. The study constructs a plant-year analytical dataset for 70 facilities from publicly accessible administrative, environmental, market, and technical records, with net social benefit treated as a constructed welfare-accounting measure based on observed records, documented public proxies, and explicit valuation assumptions. It uses plant- and year-fixed-effects models, valuation sensitivity tests, and an optimization-based decision-support layer. The descriptive evidence shows substantial welfare heterogeneity, with a mean traceable-baseline net social benefit of 17.9 euros per metric ton under the central valuation case and lower mean welfare under higher shadow prices. The fixed-effects results do not provide statistical support for the hypothesized operational drivers in the public-data panel: energy recovery, emissions intensity, availability, oxygen instability, and the interaction between emissions intensity and the shadow price index are not statistically significant. The study contributes by integrating cost accounting, externality valuation, and operational performance into a transparent public-record welfare metric for policy appraisal.