Ayman Bader

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
Environmental reporting is expanding, yet many firms achieve limited environmental improvement when carbon effects are not translated into decision-relevant cost information for budgeting, pricing, and investment appraisal. This study examines whether integrating carbon costing into activity-based costing is associated with higher carbon and environmental disclosure quality and lower carbon intensity among listed firms in Jordan. The analysis uses disclosures for 12 firms over 2018–2024 and estimates two-way fixed-effects panel models with firm-clustered standard errors to test whether within-firm changes in costing integration are followed by changes in disclosure quality and emissions intensity after controlling for firm-specific unobserved heterogeneity and common year effects. The results show no statistically significant association between costing integration and disclosure quality (β = 0.013, p > 0.10) and no significant association with carbon intensity (β = 0.238, p > 0.10). The interaction analysis further indicates that the integration–disclosure relationship is not stronger in environmentally sensitive industries (β = −0.350, p > 0.10). By contrast, firm size is positively related to disclosure quality, suggesting that visibility, organizational capacity, and reporting resources matter more than costing integration in this context. These findings indicate weak implementation depth rather than clear environmental gains. Overall, carbon-costing integration has not yet become sufficiently embedded in routine managerial practice to produce measurable improvements in disclosure quality or emissions performance in Jordanian listed firms.

Type of the article: Research Article

Abstract
Creating credibility and transparency in green finance is a persistent challenge, particularly in Islamic capital markets, where green sukuk must satisfy Shariah-compliant structuring and credible verification of environmental use of proceeds. This study examines whether blockchain adoption strengthens disclosure verifiability, improves financial reporting quality, and reduces the cost of capital in green sukuk markets. Using annual issuer-level panel data (2016–2025) from Malaysia, Indonesia, Saudi Arabia, and the UAE, we apply a staggered Difference-in-Differences design and validate the estimates using Double Machine Learning under high-dimensional controls. Blockchain adoption is associated with faster and more credible reporting, reducing audit-report lag by 12.4 days (p < 0.01), discretionary accruals by 1.8 percentage points (p < 0.05), and the probability of restatement by 8.1 percentage points (p < 0.05). Financing conditions also improve issue spreads fall by 21.7 bps (p < 0.01), secondary z-spreads by 18.3 bps (p < 0.05), and bid-ask spreads by 5.6 bps (p < 0.05). Mechanism tests show that adoption increases the Text-to-Ledger Alignment Index (δ = 0.142, p < 0.01), indicating that verifiable alignment between narratives and traceable records is a key channel. Cross-country results are strongest in Malaysia and the UAE, consistent with higher regulatory readiness. Overall, blockchain appears to function as both an integrity-enhancing reporting infrastructure and a credibility signal; scaling these benefits requires policy and standards harmonization.

Type of the article: Research Article

Abstract
FinTech growth in the Gulf has expanded digital access to banking services, but cyber-risk governance has not advanced at the same pace. This study develops and applies a quantitative framework to evaluate institutional, systemic, predictive, and probabilistic dimensions of cyber risk across Gulf financial technology ecosystems, including commercial banks, digital wallets, and payment platforms. The empirical design combined an application-level sample of ten leading mobile financial platforms with a vulnerability-level observation dataset generated through repeated static and dynamic security assessments between July 2024 and May 2025. The analysis integrated comparative statistical testing, extreme value modeling, dependency analysis, machine learning classification, and Bayesian estimation. The results revealed significant institutional divergence in vulnerability severities (p < 0.01), with Saudi Arabian Android banking applications recording the highest mean score (8.12) and UAE iOS applications the lowest (7.29). The risk distribution displayed a heavy-tailed structure, with a shape coefficient of 0.22 and a scale coefficient of 0.78, indicating that rare but severe vulnerabilities dominate exposure. Dependency modeling identified systemic linkages between platform type, regulatory environment, and vulnerability category, with correlations ranging from 0.29 to 0.36. Machine learning classification achieved 85% accuracy and 84% precision, while Bayesian estimation produced narrow 95% credibility intervals. The findings highlight distinct, quantifiable cyber-risk patterns across Gulf banks and FinTech platforms and support the need for integrated, data-driven supervisory frameworks.