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.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This study aimed at measuring the level of job burnout among King Khalid University staff. The descriptive-analytical approach was employed to describe job burnout, determine its prevalence, identify its causes, and propose ways to address it. This method was used for comparison, interpretation, and generating information to assist in understanding the phenomena of job burnout and to devise recommendations for mitigating its prevalence. The results showed that the overall mean estimation of the dimensions of the level of occupational burnout from the perspective of university staff was (2.28), with a standard deviation of (0.81), indicating a low degree. The arithmetic means of the study sample responses to the dimensions ranged from (1.98–2.66). This provides a good indicator of the prevalence of occupational burnout. The findings showed that individuals in higher ranks experience higher levels of job burnout compared to the rest of the ranks classified in the study.

This research examines intangible assets or intellectual capital (IC) performance of tourism-related industries in an underexplored area which is a tourism intensively-dependent country. In this study, VAIC which is a monetary valuation method and also the most widely applied measurement method, is utilized as the performance measurement method for quantifying IC performance to monetary values. Moreover, to better understand performance, the standard efficiency levels are further applied for classifying the performance levels of tourism industries. The sample sizes of study are 20 companies operating in the tourism-related industries in the world top travel destination or Thailand, and the companies’ data are collected from 2012 to 2021. Therefore, finally, there are 187 firm-year observations. The utilization of VAIC could assess IC performance of tourism firms and industries, and the standard efficiency levels further support the uniform interpretation of IC efficiency levels. The obtained results show the strong performance of both human and structural capital of the focused tourism dependent country especially in the logistics industry that directly supports and connects to the tourism attractions. Moreover, the finding also highlights the significance of human capital which plays as a major contributor for overall IC performance in this tourism dependent economy. This study contributes the new exploration of IC in the high impact industries and also specifically in the top significant tourism country. Moreover, the application of VAIC also confirms a practical application for management. The limited number of studied countries is a limitation of study. However, these new obtained data and information could be further applied for making comparisons or in-depth or statistical analysis in the future works.

This paper analyses wherever top executives were born and wherever they attended university to reveal regional groupings of the executives that form company culture and strategy in China and the mechanisms by which they affect corporate performance. It was found that the personal histories of top executives affect their decision-making orientation, and, in turn, company culture. The personal histories of executives and intra-regional, intra-provincial and intra-city links of corporate headquarters were obvious factors for executive selection. Distances were higher, and percentages of intra-regional links were lower for higher profit and growth companies. This shows that more competitive companies are more likely to hire executives who have lived in different regions or institutions in their lifetimes and university educations. The study concludes that Chinese firms’ key choices are influenced, in part, by external geographic factors way more advanced than the self-operation of individual enterprises.