Digital transformation is a significant phenomenon that affects almost every business sector, particularly the telecommunications industry, which is closely intertwined with information technology. This study is grounded in McLuhan’s concept of technological determinism and Martin Heidegger’s philosophy of technology, which asserts that media and technology shape human thoughts and interactions, benefiting individuals, society, and culture alike. The primary objective of this research is to investigate the environmental factors that influence digital transformation and to assess its impact on the strategic renewal of a company. This research employs exploratory qualitative methods, collecting in-depth information through interviews with the respondents from Indonesia’s leading telecommunications operator who can provide comprehensive and contextual insights into digital transformation. The findings reveal specific environmental factors that drive digital transformation. The major identified components of strategic renewal include advancements in information technology, the role of human resources, and interactions with external parties, including customers and partners.

This study investigates the roles of government and non-governmental organizations (NGOs) in constructing permanent housing for disaster-affected communities in Cianjur Regency following the November 2022 earthquake. Employing a qualitative methodology, the research utilizes in-depth interviews and field observations involving local governments, NGOs, and disaster survivors. The findings highlight the government’s central role in policy formulation, budget allocation, and coordination of housing development, while NGOs contribute through community empowerment, logistical support, and ensuring participatory planning. Challenges in collaboration, such as differing objectives and resource constraints, underscore the need for enhanced synergy. The study concludes that effective partnerships among the government, NGOs, and the community can expedite the development of sustainable, safe housing tailored to local needs. Emphasis on community empowerment and integrated resource management enhances resilience to future disasters. Success hinges on strong coordination, proactive challenge management, and inclusive stakeholder engagement throughout the recovery process.

This research aims to analyze the contribution of the Industrial World Business program in improving the work skills and work readiness of people with disabilities through the Systematic Literature Review method. The involvement of businesses and industries in developing inclusive programs for people with disabilities is an important key to bridging the skills gap and employment opportunities. This research identifies various programs, best practices, and challenges in implementing these programs. Based on the results of the literature reviewed, it was found that inclusive job training programs significantly improve the technical and non-technical skills of people with disabilities while strengthening their readiness to face a competitive job market. On the other hand, there are still obstacles in the accessibility and adaptation of training programs that must continue to be optimized. However, to achieve greater inclusivity, improvements are still needed in terms of accessibility, program adaptation, and efforts to reduce discrimination in the world of work. It is hoped that the results of this research can become a basis for policymakers, industry players, and educational institutions to continue to develop inclusive programs and empower people with disabilities in the world of work. Collaboration between industry and vocational service providers is critical to improving employment outcomes and facilitating a successful transition from education to employment for people with disabilities.

This paper aims to segment online consumers based on their attitude toward self-interest and ethical attitudes and explore the impact of these attitudes on the purchasing behavior of agricultural products online in China. The study was conducted using 633 online survey responses from consumers who have purchased agricultural products online in China. First, to validate the relationship between attitude and behavior by structural equation modeling. Next, the number of segments was determined using K-means. Finally, Pearson Chi-square difference tests were performed to analyze demographic and behavioral variables and identify each segment’s characteristics. The results of this study provide a segmentation analysis of the online market for agricultural products in China. The four segments identified are pure ethical consumers, information communicators, brand-quality pursuers, and well-heeled shoppers. Additionally, this study reveals the characteristics of each segment based on demographic and behavioral variables. This study provides a novel approach to segmenting Chinese consumers who purchase agricultural products online based on their attitudes toward self-interest and ethical attitudes, aiming to understand the impact of these attitudes on their purchasing behavior. Moreover, from an ethical consumerism perspective, it explores the effect of ethical information on purchasing agricultural products online, highlighting its significant implications for online marketing strategies.

Purpose: This study aims to clarify the meaning of sport analysis, explore the contributions derived from sporting event analysts, and highlights the importance of responsible sport gambling. It also investigates how sustainable practices can be integrated into sports analysis to enhance social well-being. Design/methodology/approach: Secondary text data from government documents, news articles, and website information were extracted by searching keywords such as sports lottery and sports analysis in traditional Chinese, and then analyzed to establish the research framework and scope. Subsequently, 18 interviews were conducted with stakeholders to gain deeper insights. Findings: The content analyses reveals that sport analysis tends to be sport data science. Sporting event analysts may contribute to improving the performance of players or a team, enhancing spectator sports, and increasing sports lottery revenues. In the leisure aspect, the professionalism of sporting event analysts not only increases epistemic and entertainment values in spectator sports but also boosts engagement with sport lotteries. To ensure these enhancements remain beneficial, it is vital to emphasize responsible sport gambling and sustainable practices that protect vulnerable groups and promote long-term health benefits for those involved in sports. The integration of sustainable practices in sport analysis and the expertise of sporting event analysts can significantly advance economic and social development by generating funds through sport lottery industry for athlete programs, sports infrastructure, and educational initiatives, aligning with multiple Sustainable Development Goals. Additionally, the professionalism of these analysts may enhance public understanding and engagement of sports, promoting increased participation in sports, reducing healthcare costs, and contributing to the development of a healthier and more resilient society. Originality: Emphasizing responsible sports gambling is essential to the sustainability of sports lotteries and the role of sporting event analysts.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.