The increasing use of social media has played a prominent role in shaping opinions and forming attitudes, especially among university students. They use them increasingly to transfer information, exchange data, and disseminate topics among students and all members of society. Therefore, this study aims to examine these networks and their role in public life, especially in shaping public opinion among university students. The study adopted a descriptive survey approach to achieve its objectives. The study was conducted on a sample of undergraduate students from four Jordanian universities, totaling 832 participants selected through purposive sampling and using the equal distribution method according to variables (gender, university, specialization). The study relied on a questionnaire as a method of data collection and filling out the data from the respondents in the questionnaire. The study found that social media plays a significant role in shaping opinions, beliefs, and ideas, and that its role is unparalleled. Also, the study showed that social media had a significant impact on shaping public opinion in Jordan among university students who use social media extensively and exchange opinions, ideas, and information, contributing to shaping a series of opinions among young people and contributing to their adoption of new ideas or changing their old ones through the dialogue facilitated by these networks, as users exchange and adopt ideas, contributing to shaping a public opinion on an issue. These findings underscore the importance of understanding and leveraging social media and online platforms to effectively communicate with and engage students.

Promoting travelling intention within social media is significant for stakeholders to grasp a new tourism market and cultivate a new model for development of tourism industry. This study aims to understand path of destination image affecting travelling intention, and to investigate the mediation role of perceived value, furthermore, to uncover the role of moderator of situational involvement. This paper conducts a survey on tourists visiting Guilin, collecting 435 questionnaires, and uses the structural equation modeling method to explore how the image of the tourism destination affects tourists’ willingness to travel. The research results indicate that cognitive image, emotional image, and projected image all have a significant positive impact on perceived value, perceived value as a significant mediator to bridge the relationship among the destination image and tourists’ travel intention. Furthermore, situational involvement plays a negative moderating role in the mediating effect of emotional value. This study endeavor will serve to enrich the understanding of perceived value theory, destination image theory, and tourism consumer behavior theory. It will also provide theoretical foundations and policy recommendations for guiding tourism consumer behavior, analyzing destination image perception, and destination marketing.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Empirical evidence suggests that generational cohorts display behavioral differences due to rapid advancements in science and technology and enhanced living standards. However, systematic studies examining the behaviours of different generations and their impact on creativity and its various antecedents are scant. This study was undertaken to bridge this gap in the literature by focusing on how generational differences could impact a few behavioural antecedents and employee creativity. The antecedent behaviours examined include self-efficacy, organizational commitment, employee empowerment, and work engagement. Data for the study was collected online using structured, standardized questionnaires. Data were collected from 432 samples and analyzed using Smart-PLS. The results show that most of the proposed antecedents impacted creativity. However, generational differences did not moderate the relationship between the antecedents and creativity. The study will interest scholars and social scientists, as it is the first to be conducted in Saudi Arabia. The study also discusses the implications and limitations. It is expected that the findings of this study will trigger more studies.

Amidst China’s escalating aging population challenge, the efficacy and quality of private elderly care services are garnering increasing scrutiny. This research focuses on evaluating how service quality and customer perceived value influence the loyalty of elderly clients, with customer satisfaction acting as a mediating factor. Grounded in established service quality frameworks and loyalty theories, the study utilizes a quantitative methodology, administering surveys across eight private elderly care institutions in H city, China. A total of 600 surveys were collected, providing a comprehensive data set that encompasses five dimensions of service quality—tangibility, assurance, responsiveness, reliability, and empathy—as well as customer perceived value, satisfaction, and loyalty. Structural Equation Modeling (SEM) was employed to validate the hypothesized relationships. Findings reveal that service quality significantly boosts customer perceived value and satisfaction, which in turn markedly enhance customer loyalty. Notably, customer satisfaction emerged as a crucial mediator between service quality and loyalty, as well as between perceived value and loyalty. This study not only advances theoretical understanding of service quality impacts but also offers actionable insights for enhancing service delivery and customer loyalty in the context of private elderly care.

The COVID-19 pandemic occasioned significant changes in many aspects of human life. The education system is one of the most impacted sectors during the pandemic. With the contagious nature of the disease, governments around the world encouraged social distancing between individuals to prevent the spread of the virus. This led to the shutdown of many academic institutions, to avoid mass gatherings and overcrowded places. Developed and developing countries either postponed their academic activities or used digital technologies to reach learners remotely. The study examined the benefits of online learning during the COVID-19 pandemic. The participants for the study consist of 5 lecturers and 30 students from the ML Sultan Campus of the Durban University of Technology, South Africa. Data was collected using open-ended interviews. Content analysis was applied to analyze the data collected. Data was collected until it was saturated. Different ways were implemented to make online learning and teaching successful. The findings identified that the benefits of online learning were that it promotes independent learning, flexible learning adaptability and others.