This research examines the influence of virtual community platform attributes on luxury consumers’ purchase intentions, with a specific focus on the role of policy innovation in digital infrastructure. The study aims to 1) identify key factors affecting purchase intentions toward luxury products in virtual environments; 2) develop and validate a structural equation model to analyze these intentions; and 3) provide actionable insights for luxury goods marketers to refine their strategies within these platforms. Utilizing a structural equation model, the study investigates the interactions among various determinants of consumer behavior in virtual communities, highlighting the impact of policy innovation. Data was collected through purposive sampling from 1142 respondents in China’s top 10 high-spending cities on luxury goods, ensuring data relevance. The findings emphasize the significance of knowledge sharing, interactive communication, and leaders’ opinions in virtual communities in building consumer trust and shaping perceptions of online reviews. These elements influence purchase intentions directly and indirectly, with consumer trust serving as a crucial mediator. The study reveals the substantial impact of virtual community attributes on fostering consumer trust and shaping buying decisions for luxury items, underlining the contribution of social development processes. Moreover, the role of policy innovation is found to be significant in enhancing these virtual community dynamics, suggesting that regulatory changes can positively influence consumer engagement and trust. The conclusions offer valuable implications for marketers, proposing strategies to boost consumer engagement and drive sales in virtual settings. This research contributes to the theoretical understanding of digital consumer behavior and provides practical strategies for innovation and growth within the luxury goods sector, emphasizing the critical role of policy innovation in shaping these dynamics.

This research examines the interplay between human dignity and the pursuit of knowledge within Islamic thought, using insights from the Quran. It explores how Islamic epistemology emphasizes the harmonious integration of divine revelation and human reason, underscoring the importance of knowledge as a key factor in both intellectual and spiritual development. By analyzing the contributions of classical Islamic scholars, such as Al-Farabi, Ibn Sina, and Al-Ghazali, alongside Western epistemological traditions, the study highlights complementary and contrasting approaches to understanding knowledge and its role in shaping ethics and governance. Furthermore, the research draws on contemporary case studies, such as the Marrakesh Declaration and Masdar City, to illustrate how Quranic principles of cooperation, justice, and environmental stewardship can inform modern societal frameworks. Ultimately, the study argues for the continued relevance of Islamic thought in addressing contemporary global challenges, emphasizing that the pursuit of knowledge not only advances scientific discovery but also promotes human dignity, justice, and societal well-being.

This research aims to analyze the relationship between financial literacy variables and financial inclusion, the relationship between financial literacy variables and financial technology, and the relationship between financial technology variables and financial inclusion. The analysis of this research is to learn more about how financial literacy and the use of financial technology influence financial inclusion. This type of research is associative quantitative. Next, the relationship between these variables is explained using statistical formulas. Consequently, the term for this research is “quantitative research”. The study population is the number of people who use financial services. For this sampling, the purposive random sampling method was used. The following criteria are determined in sampling: 1) Minimum age 17 years, this is intended to take the minimum age standard in sampling and is considered capable of understanding the contents of the questionnaire statements. 2) Have ever used financial services. In this study, 11 question items were used to measure 3 variables, so this study used the largest range, namely 231 respondents. The intervention variable will be used as a reference for the Partial Least Square (PLS) method to analyze this research data. This study uses a causal model (causal modelling, relationships, and influence) or path analysis. The hypothesis that will be discussed in this research is tested using the Structural Equation Model (SEM), which is operated with Smart PLS. The results of this research show that financial literacy has a positive and significant impact on financial inclusion in society. Financial literacy has a positive and significant impact on financial technology. financial technology has a positive and significant impact on financial inclusion, financial technology can offset the impact of financial literacy on financial inclusion. The results of this research are used as input for the community so that they pay more attention to their internal human resources related to financial products that can be used for investment. With knowledge of the right financial products, it is hoped that they can create good financial behaviour so that an awareness of the importance of carrying out good financial planning. For financial institutions, it is hoped that this can increase easy access to financial products and services, in particular credit for businesses as additional capital for the community.

Universities continue to provide solutions to private and public sectors of the economy by providing a skilled economy, increasing employment potentials, and improving employee performance. This study offered a theoretical model on the contributing factors to graduate employability among student entrepreneurs in Malaysian Higher Education and the mediating mechanism of perceived support and usefulness in social entrepreneurship to solve the graduate unemployment problem. We attained data using purposive and face-to-face sampling methods with acceptable data from 296 undergraduates and analyzed with the SEM software from respondents of various cultural backgrounds. Findings suggest a positive significant relationship between motivations, skills in social entrepreneurship, knowledge, and social elements on graduate employability. Similarly, perceived support explained skills, knowledge and social elements’ relationship to graduate employability except for perceived usefulness. The outcome further discovered the perceived support role for graduates of social entrepreneurship in fostering job crafting and future employability with various implications and recommendations. The results require the application of other research approaches to provide concrete implementations and social and economic solutions. Insightful results and proposals helpful to policymakers like higher education curricula developers and implementers, scholars, government and private universities of this study can help curb graduate unemployment through social entrepreneurship.

The concept of a “community with Shared Future for Mankind” was first proposed in China and has quickly become an integral part of discussions on international relations and global governance. This concept originates from China’s profound insights into the interdependence of nations in the context of globalization, recognizing that the fates of countries are closely interconnected when facing global challenges. With the shifting balance of international forces and the increasing severity of global issues, traditional mechanisms of global governance have shown certain delays and inadequacies. From the difficult birth of climate change agreements to frequent conflicts in international security, from the uneven development brought by economic globalization to the ethical and management issues of emerging technologies, the structure of global governance faces unprecedented challenges. This paper focuses on the research question of how the concept of a “community with Shared Future for Mankind” aligns with and transcends the existing global governance system, using theoretical analysis and practical references for discussion. The findings suggest that the concept provides new ideas and frameworks for addressing global challenges such as climate change and international security, promoting the democratization and efficiency of global governance, especially in enhancing the representativeness and discourse power of developing countries in global decision-making. Additionally, the research identifies the transcendent nature of the concept in global governance, aiming to offer possible directions and strategies for the future development of global governance.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.