Ahlia University

This research analyzed the role of Green Marketing (GM) and Artificial Intelligence (AI) in promoting Corporate Sustainability (CS) across the environmental, social, and economic dimensions within the industrial sector in the Palestinian territories. Given the limited empirical evidence from developing and resource-constrained contexts, an explanatory sequential mixed-methods design was employed. The quantitative phase involved a survey of 500 valid respondents, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The quantitative findings were complemented by fifteen in-depth semi-structured interviews to further interpret and validate the survey results. The results indicate that GM showed the largest effect size and functions as a strategic approach for embedding sustainability values into organizational activities. AI also demonstrated a positive and supportive role by enhancing operational efficiency and monitoring capabilities within industrial processes. The interaction between AI and GM showed a statistically significant but relatively small effect, particularly in the social sustainability dimension, suggesting that AI may help reinforce the effectiveness of green marketing practices. The qualitative findings further illustrate how GM contributes to internal accountability, eco-design initiatives, stakeholder trust, and competitive positioning, while AI supports waste management, resource optimization, employee safety monitoring, forecasting accuracy, and sustainability reporting verification. Overall, the results suggest that GM and AI jointly contribute to improving corporate sustainability practices, with GM providing strategic direction and AI supporting operational implementation. This study contributes to the literature on sustainability, marketing, and digital transformation by providing empirical evidence on the interaction between green marketing and artificial intelligence in promoting corporate sustainability within a developing-country context.

Purpose - This study tests how cybersecurity awareness, authentic/quality assessment design and perceived deterrence jointly shape academic-integrity climate and, through it, strengthen AACSB accreditation alignment in GCC business schools. This research position climate as the conduit translating secure behavior and assessment practices into credible Assurance-of-Learning evidence under AACSB Standard 5. The research further argues that accreditation maturity - years since initial accreditation or latest CIR - amplifies the climate alignment link by institutionalizing routines, calibrated rubrics, versioned repositories and audit trails. Recasting alignment as a second-order dependent construct, the study links integrity governance with accreditation outcomes and offers a theory of change for rapidly digitizing GCC contexts. practical. Design/methodology/approach - Multisource, cross-sectional data were collected across six GCC countries from students (cybersecurity awareness, integrity climate), faculty/course leads (authentic/quality assessment design) and administrators (accreditation maturity), with external experts rating AACSB alignment via an Assurance-of-Learning rubric anchored to Standard 5. After screening, the final dataset comprised 482 valid cases. Analyses used SmartPLS 4 with nonparametric bootstrapping (10,000 subsamples). Measurement quality met accepted thresholds (Cronbach's a = 0.86, composite reliability = 0.90, AVE = 0.61; HTMT <0.85); common-method bias checks indicated no problematic inflation (full-collinearity VIFs = 2.50; marker path nonsignificant). Predictive strength and fit were adequate (R(2)AIC = 0.56; R(2)AACSB = 0.49; SRMR = 0.061; Q(2) > 0). Findings - All three antecedents positively predicted AACSB alignment: cybersecurity awareness (beta = 0.18, p = 0.001), authentic/quality assessment design (beta = 0.24, p < 0.001) and perceived deterrence (beta = 0.12, p = 0.041). Academic-integrity climate was the central mechanism, partially mediating each antecedent's effect on alignment. Accreditation maturity significantly strengthened the climate? alignment pathway, showing that schools further along in accreditation translate integrity norms into standards-consistent evidence more effectively. Predictive power was substantive for the mediator and outcome (R(2)AIC = 0.56; R(2)AACSB = 0.49), and demographic controls on alignment were nonsignificant. Overall, secure behavior, authentic design and credible deterrence operate chiefly through organizational climate, with maturity amplifying this conduit in rapidly digitizing GCC contexts. empirically robust. Research limitations/implications - Cross-sectional, multisource data limit causal inference; several constructs include self-report, and expert rubric ratings - though reliable - may reflect rater stringency and documentation differences. Generalizability is bound to AACSB-pursuing GCC schools, and unmeasured institutional factors (e.g. IT governance, LMS analytics) may bias estimates. Integrity dynamics may also shift as generative-AI policies evolve. Practically, deans and AoL leaders should pair evidence-based awareness programs with authentic, process-revealing assessments; maintain credible deterrence with transparent sanctions; manage integrity climate as a KPI; and invest in maturity enablers: stable AoL committees, calibrated rubrics, version-controlled repositories, auditable trails and analytics dashboards that sustain the climate alignment linkage documented here. Originality/value - The study's novelty lies in modeling accreditation alignment as a measurable dependent construct and integrating security behavior and assessment design into a single mediated-moderated framework.

Traffic flow forecasting is vital but challenging in traffic management. This study proposes a complex gray relational degree-based network to cluster traffic flow observation nodes using the Louvain algorithm. The method effectively distinguishes the nodes, providing accurate clustering for equivalent training sets and traffic flow prediction. To capture the characteristics of time series data in traffic flow data, the gated recurring unit (GRU) is the backbone network. For improved accuracy, a multi-period Exattention-GRU model is developed. GRU enhances network performance as the main component with an external attention mechanism and a residual structure. Stacking and fusing the improved GRU network captures various traffic information, which contributes to improving accuracy. The model, trained on an equivalent set using the RAdam algorithm, demonstrates effectiveness with favorable prediction results across various datasets and traffic conditions. Taking into account 36 data sets, the average metrics for RMSE, MAPE and MAE are recorded as 60.6363, 7.40%, and 41.4897.

Religiosity is increasingly recognised as shaping entrepreneurial behaviour, yet existing models often overlook how it influences technology adoption during crises. This study examines the mediating role of religiosity—disaggregated into intrinsic and extrinsic dimensions—in the relationship between technology adoption and entrepreneurial resilience in an Arab-Muslim context during the recent health crisis (COVID-19). Drawing on structural equation modelling (SEM) and AI-assisted observational data, the analysis shows that technology adoption enhances resilience only when aligned with extrinsic religiosity rooted in social expectations. In contrast, intrinsic religiosity, tied to spiritual beliefs, shows no direct effect on resilience. Contributing to resilience theory in entrepreneurship, this study reconceptualises religiosity as a dynamic strategic resource rather than a static background variable. A novel religiosity scale, developed for this research, introduces a context-sensitive tool for measuring religious influence on female entrepreneurial behaviour, with implications for policy design and support initiatives in socio-religiously restricted environments. When Muslim female entrepreneurs adopt digital technologies in ways that align with socially expected religious norms (extrinsic religiosity), they are more likely to sustain business continuity during crises like COVID-19. Using survey and observational data, this study shows that extrinsic religiosity provides social legitimacy for digital adoption, enabling women to navigate socially restrictive environments effectively. This legitimising role is especially pronounced among older and married entrepreneurs, who are often embedded within religious and community structures. In contrast, intrinsic religiosity provides ethical guidance but does not directly drive adaptive business strategies needed to sustain operations during crisis contexts. By unpacking these two dimensions of religiosity, this study demonstrates that religious identity is not just a background characteristic but an active force shaping how women adapt in restrictive contexts. These findings challenge the common assumption that religion holds Muslim women back, revealing instead that, under the right conditions, it can help them move forward. Practically, digital support programmes should be co-designed with religious institutions and Sharia-compliant business mentors to ensure technology strategies resonate with the culturally grounded moral and institutional logics that structure female entrepreneurial contexts.

This research seeks to empirically explore the mediating role of governance quality on the nexus between generative artificial intelligence and macro-level financial performance. This empirical study employs cross-country panel data from 2021 to 2024, encompassing an analytical sample of nine emerging markets. Initially, various static panel data techniques were employed. Afterward, to alleviate potential endogeneity bias and support the reliability of the results, the one-step system GMM approach was implemented. The results reveal that while fixed-effects estimates indicate a partial mediating role of governance quality in the nexus between generative artificial intelligence and macro-level financial performance, the dynamic system GMM estimations support full mediation once endogeneity and path dependence are controlled for. Taken together, these findings underscore the central role of governance quality as the primary transmission channel through which AI readiness translates into macro-level financial performance. The novelty of this research is reflected in its application of mediation techniques to elucidate the relationship between generative AI and macro-level financial performance. Moreover, this study pays rigorous attention to offer multidimensional insights for regulators and policymakers to design solid regulatory frameworks that enhance the adoption of generative AI tools, uphold high governance standards, and ultimately strengthen macro-level financial performance.