Inclusive heritage tourism has received growing scholarly attention, but there is little empirical research about the role of experience design in the creation of value among visually-impaired tourists. This work assessment evaluates the effects of gamification and multidimensional immersive senses on perceived value through experience quality in the context of heritage tourism in Shanxi, China. Using the Stimulus, Organism, and Response (S-O-R) theoretical framework, the gamification and multisensory immersion are presented as the types of experiences as stimulus, the experience (experiential quality) as the mediator, and perceived value as the response outcome. A quantitative cross section survey was distributed to the visually impaired tourists who have visited at least one heritage tourism site in Shanxi during the past twelve months. A total of 278 valid responses were analyzed by a partial least squares structural equation modelling (PLS-SEM). Results show that both gamification and multisensory immersion make a significant contribution to the eminence of the experiential quality. Experiential quality in turn becomes the strongest predictor of perceived value. In addition, gamification has a large direct impact on perceived value, while multisensory immersion has a direct impact on the perceived value via experiential quality. These findings therefore suggest that value on inclusive heritage tourism is not only dependent on the presence of interactive or sensory features, but rather on the degree in which these are integrated into an integral, accessible and meaningful experience. The present study enhances the use of the S-O-R framework into an accessibility-sensitive heritage context and emphasizes the primary role of experience design leading to effective implementation of meaningful participation, value creation for visually-impaired tourists.
This study investigates the impact of financial distress on earnings management among Thailand-listed companies during the COVID-19 recovery period (2021-2023). Using 1,323 firm-year observations, this study compared the Modified Jones Model and the Yoon Model to determine the most appropriate framework for emerging markets. The results demonstrate that the Yoon Model provides superior explanatory power for the Thai context. Furthermore, empirical evidence reveals that financial distress significantly and positively influences earnings management. This suggests that distressed firms utilize discretionary accruals to project a favorable financial image to investors and creditors, as well as to enhance their ability to attract new investment, suggesting that earnings management serves as a mechanism employed by executives to obscure underlying financial problems.
The study shows how blockchain security is used to assess the performance and reliability of smart Waqf sukuk in Islamic philanthropic finance. It lurks on how the distinguishing characteristics of block chain i.e., decentralization, immutability, cryptographic integrity and consensus mechanisms can be used to elevate the degree of transparency, trust and operational efficiency of the digital sukuk constructions hinged on the principles of Waqf. Another question that is established in the study is whether the cybersecurity technologies play a moderating role in improving the resilience and stability of the smart Waqf sukuk systems regarding the advanced authentication systems, intrusion detection software and artificially intelligence-based threat detection systems. It is implied to base on a quantitative research methodology and apply structural equation modeling as the tool of assessing the interdependence of blockchain security and cybersecurity technologies, as well as smart Waqf sukuk performance. It is predicted that the study will prove the close relationship between the increased blockchain security and reliability, transparency and effectiveness of smart Waqf sukuk platforms. It is also expected that cybersecurity technologies will improve this relationship by minimizing the vulnerabilities, enhancing the system resilience and enhancing the protection of the digital resources and transactions. The anticipated conclusions will also lead to the creation of secure Islamic financial instruments by demonstrating that the combination of strong blockchain security with modern cybersecurity systems will increase the trust of the stakeholders, maintain the integrity of assets and help sustainably manage Waqf resources. Apart from the regulatory implications the paper has raised to the regulators in order to move forward with the digital governance of Waqf and improve smart sukuk ecosystems that are more secure and reliable, it also gave regulatory suggestions to Islamic financial institutions and technology developers. This study is conceptual and analytical in nature, and the data presented are representative examples to illustrate the concept being presented rather than actual empirical results. The findings suggest that the hypotheses are partially supported, as the blockchain security dimensions statistically significantly affect the Smart Waqf Sukuk, but some of the hypothesized relationships (Consensus Mechanisms and Cybersecurity Technologies) are opposite to the reality, and the moderating effect of cybersecurity technologies is only statistically significant for the relationship between Consensus Mechanisms.
Improving student success and supporting evidence-based decision-making in higher education depend on early detection of academically at-risk students. An Explainable Artificial Intelligence (XAI) framework is proposed in this paper for multimodal student performance prediction to support institutional decision-making at Prince Sattam bin Abdulaziz University (PSAU). The framework was developed and evaluated using a publicly available educational dataset collected from a higher education institution, integrating data from the Student Information System (SIS), Moodle Learning Management System (LMS), and the eDify mobile learning application. The proposed framework combines comprehensive data preprocessing, Mutual Information-based feature selection, Optuna-based hyperparameter optimization, and an optimized CatBoost classifier. Using repeated stratified 5-fold cross-validation, the model's performance was assessed and compared with statistical learning methods, conventional machine learning algorithms, homogeneous ensemble methods, and heterogeneous ensemble approaches. The proposed model demonstrated superior predictive efficacy, attaining an Accuracy of 92.0%, Precision of 92.7%, Recall of 97.9%, F1-score of 95.2%, ROC-AUC of 0.913, PR-AUC of 0.974, Balanced Accuracy of 0.824, and Matthews Correlation Coefficient of 0.726, significantly exceeding all rival models according to the Wilcoxon signed-rank test (p < 0.001). Mutual Information feature selection further improved predictive performance while reducing model complexity. Explainability was achieved through CatBoost feature importance and SHAP analyses, which consistently identified assessment outcomes and online learning engagement variables as the most influential predictors. The proposed framework provides a transferable blueprint for implementing explainable early-warning systems at Prince Sattam bin Abdulaziz University and other higher education institutions to support timely interventions, personalized learning support, and evidence-based institutional decision-making.
The Environmental Phillips Curve (EPC) is understood as an effort to highlight the relationship between environmental quality and unemployment. research testing the relationship variations in the spatial influence of unemployment, regional income, human development, electricity consumption, population growth and pandemic-year dummy on environmental degradation in Indonesia within the EPC concept framework. This study uses the Geographically Weighted Panel Regression method. The GWPR method is used to show variations in the influence between independent and dependent variables across the characteristics of each province in Indonesia. GWPR test results, the variables of unemployment, human development, and electricity consumption can reduce environmental degradation in Indonesia, with varying levels of significance across provinces. Regional income and population growth increases environmental degradation in Indonesia. This indicates that the EPC concept has been proven in Indonesia but varies by province, depending on each province's economic structure. The findings of this study are that the EPC is proven in regions whose economies rely on the extractive sector, mining, manufacturing industry, transportation, and tourism. These findings imply that employment and environmental policies should be tailored to regional economic structures, promoting green employment in extractive regions and accelerating low-carbon industrial transformation in manufacturing- and service-based provinces.
