Adopting new technologies in Jordanian universities, namely cloud services, represents change in practices that needs to be investigated, as it is expected to face resistance in adoption by faculty members and staff that are used to old practices. A dedicated questionnaire was constructed based on the UTAUT model in order to identify the factors affecting the behavioral intentions leading to use new technologies. Five Jordanian universities participated in this study, and the results showed that there is a high behavioral intention (BI) among staff and faculty members to use cloud services and solutions within their workplace. This research showed that there are factors positively affecting the adoption of cloud services in Jordanian universities, and negative factors have been identified too. University management and staff members needs to be introduced to these factors in order to have better judgement on the future investment and practices of using new technologies.

The adoption of new technologies in Jordanian Universities related to cloud services, shows differences in practices between faculty and staff members. Resistance to adoption may accrue by faculty and staff members who are accustomed and favoring old practices. A questionnaire was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model to identify factors that affect behavioral intentions that lead to the use of mobile cloud computing during the covid-19 pandemic, taking into consideration Work-type as the mediating factor. Five Jordanian Universities participated in this study, with a total response of 153 faculty and staff members. The conceptual proposed model was tested to ensure the fitness of the structural model for providing correct estimations. The collected sample was subjected to confirmatory factor analysis to ensure construct, convergent and discriminant validity. The results came positive in terms of composite reliability as they were above 0.70, for Average Variance Extracted (AVE) it came more than 0.05and Cronbach alpha exceeded 0.70. The results revealed the fitness of the proposed model to measure differences in behavioral intentions towards adopting mobile cloud services between faculty members and employees. Moreover, the results showed that work type had some interesting moderating impact on the tested relationships. Moreover, the results showed that there is a high Behavioral Intention (BI) between faculty and staff to use mobile cloud services and solutions within their workplace. In addition, the results showed some inequalities of the behavioral intention toward the adoption of mobile cloud services in Jordanian Universities between the two groups. These results call the university administration to clarify these factors for user groups to obtain a better judgment on investment and future practices for using new technologies.

Uncertainty of getting admission into universities / institutions is one of the global challenges in academic environment. The students are having good marks with high credential but not sure about getting their admission into universities / institutions.  In this research study the researcher built a predictive model using Naïve Bayes Classifiers –machine learning algorithm to extract and analyze hidden pattern in students’ academic records and their credentials. The main objective of this research study is to reduce uncertainty for getting admission into universities / institutions on the basis of their previous credentials and some other essentials parameters. This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) approach to predict student’s admission into universities or any higher institutions.  The predictive model is built on training dataset of students’ examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictor accuracy rate of 72% and has been experimentally verified.  To improve the quality of accuracy of predictive model the researcher used the Shapiro-Walk Normality Test and Gaussian distribution on large datasets. The predictive model helps in reducing the admission uncertainty and enhances the universities decision making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to students’ admission into universities or any higher academic institutions, and it demonstrates that many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention.