Enterprise green innovation drives sustainable development and contributes to the realization of a ‘beautiful China’. It enhances resource utilization, reduces energy consumption, and achieves economic-environmental objectives through technological advancements. This paper examines the impact of the gender composition of a company’s CEO and CFO on green innovation by empirical research method using the data of the firms listed on Chinese capital market from 2015 to 2022. Our findings indicate that: (1) Male CEOs and CFOs are more likely to promote green innovation compared to their female counterparts; (2) Leadership teams comprising opposite-sex pairs tend to weaken the promotion of green innovation. These conclusions are consistent across state-owned enterprises and within the manufacturing sector. This study provides a novel perspective on enterprise green innovation, offering insights for companies regarding their green innovation strategies and for policymakers in shaping relevant policies.

The study examined the mediating role of supply chain security performance on the relationship between supply chain security practices and supply chain disruptions occurrences in the manufacturing industry in Ghana. Drawing on a survey of 336 manufacturing firms, dynamic capability, and contingency theories were applied using structural equation modeling (SEM) to test the conceptual model. It was discovered that both direct and indirect hypotheses supported the findings of the study. The results indicate that Ghanaian manufacturing firms have made progress in implementing supply chain security measures. The findings revealed that the adoption of comprehensive supply chain security practices is positively associated with improved performance metrics, including reduced inventory losses and damages, faster order fulfillment and delivery times, lower costs related to security incidents, and enhanced brand reputation and customer trust. Policymakers can leverage these insights to develop support programs aimed at strengthening the security capabilities of manufacturing firms, ensuring they are equipped to compete effectively in both local and global markets, improving security performance, and reducing the likelihood and impact of supply chain disruptions. In the quest of bridging the gap between theory and practice, this research contributes valuable knowledge to the discourse on supply chain security in developing countries, offering a roadmap for enhancing resilience and performance in the manufacturing sector.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

The aim of this study is to determine how bank diversification affects bank stability. To this end, it examines data of 136 commercial banks operating in 14 MENA (Middle East and North Africa) countries observed from 2005 to 2021, using the System Generalized Method of Moments (GMM) panel data regression analysis. The selected countries are Bahrain, Egypt, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Morocco, Lebanon, Algeria, Tunisia, Iran, Iraq, and the United Arab Emirates. The main results point to the enhancing effect of income diversification on bank stability. Our results underline the “Bright Side” of banking income diversification in the MENA region. However, this stabilizing income diversification effect is not always maintainable. The results also point to a non-linear relationship between interest/non-interest income and financial stability, suggesting that higher diversification reduces risk. We use a dynamic panel threshold model to determine income diversification thresholds that stabilize banks in the MENA region.

Purpose—Quality service plays a significant role in enhancing customer satisfaction and loyalty. The main objective of this research is to investigate the effect of Salalah port service quality on customer satisfaction. Design/methodology/approach—This paper used a quantitative research design. Data were collected from 300 repeated customer of Salalah Port in Oman. Statistical Package (SPSS) version 25.0 was used for analysis of data and adopted to test the hypothesized model. Findings—The research findings confirm the positive influence of the five dimensions of service quality – tangible, empathy, reliability, responsiveness, assurance (TERRA) on customer satisfaction. Originality/value—The findings of this study develop the literature by adding empirical research evidence that the TERRA of Salalah port service quality which have a significant effect on customer satisfaction. The result also provide evidence from the Arab region where the data and research in this region are limited.

This study introduces an innovative approach to assessing seismic risks and urban vulnerabilities in Nador, a coastal city in northeastern Morocco at the convergence of the African and Eurasian tectonic plates. By integrating advanced spatial datasets, including Landsat 8–9 OLI imagery, Digital Elevation Models (DEM), and seismic intensity metrics, the research develops a robust urban vulnerability index model. This model incorporates urban land cover dynamics, topography, and seismic activity to identify high-risk zones. The application of Landsat 8–9 OLI data enables precise monitoring of urban expansion and environmental changes, while DEM analysis reveals critical topographical factors, such as slope instability, contributing to landslide susceptibility. Seismic intensity metrics further enhance the model by quantifying earthquake risk based on historical event frequency and magnitude. The calculation based on higher density in urban areas, allowing for a more accurate representation of seismic vulnerability in densely populated areas. The modeling of seismic intensity reveals that the most susceptible impact area is located in the southern part of Nador, where approximately 50% of the urban surface covering 1780.5 hectares is at significant risk of earthquake disaster due to vulnerable geological formations, such as unconsolidated sediments. While the findings provide valuable insights into urban vulnerabilities, some uncertainties remain, particularly due to the reliance on historical seismic data and the resolution of spatial datasets, which may limit the precision of risk estimations in less densely populated areas. Additionally, future urban expansion and environmental changes could alter vulnerability patterns, underscoring the need for continuous monitoring and model refinement. Nonetheless, this research offers actionable recommendations for local policymakers to enhance urban planning, enforce earthquake-resistant building codes, and establish early warning systems. The methodology also contributes to the global discourse on urban resilience in seismically active regions, offering a transferable framework for assessing vulnerability in other coastal cities with similar tectonic risks.