In higher education institutions, effective digital communication is crucial for achieving administrative goals, such as improving student services, managing resources, and facilitating collaboration among staff members. By exploring the impact of copresence factors on digital communication effectiveness, higher education institutions can gain a deeper understanding of the factors that influence their digital communication and develop strategies that optimize its efficiency. The study applied a quantitative research approach through a questionnaire survey to collect required responses from employees who are working in the higher education institutions of Jordan with a total of 304 participants. The findings of this study indicate that copresence factors play a significant role in the effectiveness of digital communication within higher education institutions in Jordan. The results support the framework developed by others and suggest that self-copresence and partner-copresence have a positive impact on the efficiency of communication. This highlights the importance of considering the presence of individuals during digital communication and the impact it can have on the quality of the exchange. In conclusion, the study sheds light on the importance of correspondence in digital communication and its impact on the efficiency of communication within higher education institutions. The findings can help in the development of strategies and practices for enhancing the effectiveness of digital communication and improving administrative work in higher education institutions in Jordan.

This study aims to evaluate tourists' reviews of gastronomy tourism expressed in Google reviews according to the CAC model (Cognitive, Affective, and Conative), and to examine the inter-correlations between CAC model components. The study was applied to traditional restaurants in Amman downtown. The research then extracts the main themes from the textual reviews as well as a sentiment score of an affective image of traditional Amman downtown restaurants. The results of machine learning experiments suggest that the proposed approach can identify traditional restaurant reviews in Amman downtown into CAC model components. The results also show that the Random Forest algorithm performed best in the cognitive and cognitive dimensions, whereas the Neural Network algorithm performed best in the affective dimension. ML classifier revealed that most of the reviews were classified as cognitive (such as the type of food, and services) while the remaining reviews were classified as affective (such as pleasure and arousal) and conative (such as intention to recommend, and positive word of mouth) respectively. The highest probability of the cognitive components was the traditional food topic reflecting the unique image of Jordanian traditional food. Affective images formed by users were mainly positive emotions, indicating that the destination image spread well.