This study investigates the impact of supply chain agility on customer value and customer trust while investigating the role of price sensitivity as a mediating variable in the healthcare industry. A quantitative methodological approach was used. This was cross-sectional descriptive research based on a survey method, and data were collected using a structured questionnaire. The sample consisted of 384 respondents who had already used healthcare facilities. The sampling technique was convenience sampling and collected data were analyzed using structural equation modeling. The study indicated that supply chain agility positively impacts customer value and customer trust, while there is no moderation role of price sensitivity in the healthcare industry. Previous scholars revealed that there is a strongly available association between supply chain agility and customer value. But no attempt was undertaken to investigate the impact of supply chain agility on customer trust while moderating the role of price sensitivity.

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.

Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.

This study aims to have a more diversified view of the online visibility through attempting to evaluate the effectiveness of various SEO strategies in placing on website in search engine result. This research involves 400 respondents where it checks how keywords as one of the SEO strategies affect website ranking as well as technical SEO and off-page strategies. The appropriateness of the relevant keyword, as the result shows, there is a significant connection with the website ranking, closely trailing the importance of technical SEO in positioning the website on the first page, exerting a pronounced impact. While off-page strategies are the third most dazzling one: a significant degree of its residence/impact on website ranking. This research is a significant contribution to the field of digital marketing and its literature as it delivers an in-depth understanding on the major factors that affects online visibility and website ranking.

The purpose of this study was to examine the effect of E-integrated marketing communication on consumers’ purchasing behavior of mobile services. The population for the study involves all orange telecom mobile service customers in Jordan. Three hundred ninety-five questionnaires were distributed to orange telecom customers in Jordan, however, 375 only returned, which has been used for analysis. structural equation modeling using programs such as AMOS was used to investigate the impact of E-integrated marketing communication on consumers’ purchasing behavior. Data was collected through questionnaires was sent to study sample. The results of the study showed that E-integrated marketing communication had a positive impact on consumers’ purchasing behavior. Based on the findings, the study recommended that Orange Telecom should focus more on e-public relations to create a favorable image of the company among different groups of consumers, which can potentially enhance their purchasing behavior towards its mobile services. It is imperative for Orange Telecom to prioritize its e-integrated marketing communication strategy to effectively reach out to its target audience and influence their purchase decisions.

Low enrollment intention threatens the funding pools of rural insurance schemes in developing countries. The purpose of this study is to investigate how social capital enhances the enrollment of health insurance among rural middle-aged and elderly. We propose that social capital directly increases health insurance enrollment, while indirectly influences health insurance through health risk avoidance. We used data from the China Health and Retirement Longitudinal Study (wave 4) dating the year of 2018, instrumental variable estimation was introduced to deal with the endogeneity problem, and the mediation analysis was used to examine the mechanism of social capital on insurance enrollment. The results show that social capital is positively related to social health insurance enrollment, and the relationship between social capital and social health insurance enrollment is mediated by health risk avoidance.