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.

Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.

This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.

This paper explores the integration of digital technologies and tools in English as a Foreign Language (EFL) learning in Jordanian Higher Education through a qualitative open-ended online survey. It highlights the perceptions of 100 Jordanian EFL instructors, each with a minimum of five years of experience, on the digital transformation in the EFL learning process. The survey, consisting of ten open-ended questions, gathered in-depth insights on the benefits, challenges, and implications of this transformation. Thematic analysis was employed to analyze the qualitative data, revealing varied levels of experience, the use of diverse digital tools, and both technical and pedagogical challenges. Key findings include the positive impact of digital tools on teaching and learning experiences, enhanced student engagement, and opportunities for personalized learning and collaboration. The study concludes that leveraging digital resources can enhance EFL learner engagement and learning outcomes, inform future pedagogical practices, and shape the landscape of digital transformation in EFL Higher Education for years to come.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.