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.

[1] Abdullahi M., Salleh N., Nordin A., and Alwan A., “Cloud-Based Learning System for Improving Students’ Programming Skills and Self-Efficacy,” Journal of Information and Communication Technology, vol. 17, no. 4, pp. 629-651, 2020.

[2] Ajzen I. and Madden T., “Prediction of Goal Directed Behaviors: Attitudes, Intentions, and Perceived Behavioral Control,” Journal of Experimental Social Psychology, vol. 22, no. 5, pp. 453-474, 1986.

[3] Ajzen I., “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes, vol. 50, no. 2, pp. 179-211, 1991.

[4] Al Rawajbeh M., Al Hadid I., Aqaba J., and Al- Zoubi H., “Adoption of Cloud Computing in 212 The International Arab Journal of Information Technology, Vol. 19, No. 2, March 2022 Higher Education Sector: an Overview,” Indian Journal of Science and Technology, vol. 5, no. 1, pp. 23-29, 2019.

[5] Aljamal R., El-Mousa A., and Jubair F., “A Comparative Review of High-Performance Computing Major Cloud Service Providers,” in Proceedings of 9th International Conference on Information and Communication Systems Irbid, pp. 181-186, 2018.

[6] Almaiah M. and Al-Khasawneh A., “Investigating the Main Determinants of Mobile Cloud Computing Adoption in University Campus,” Education and Information Technologies, vol. 25, pp. 1-21, 2020.

[7] Alzakholi O., Haij L., Shukur H., Zebari R., Abas S., and Sadeeq M., “Comparison Among Cloud Technologies and Cloud Performance,” Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 40-47, 2020.

[8] Arpaci I., “Hybrid Modeling Approach for Predicting The Educational Use of Mobile Cloud Computing Services in Higher Education,” Computers in Human Behavior, vol. 90, pp. 181-187, 2019.

[9] Awanto A., Ardianto Y., and Prasetya A., “UTAUT Model Implementation on User Behavior in Use of Information Technology,” Jurnal Teknologi Dan Manajemen Informatika, vol. 6, no. 1, pp. 53-59, 2020.

[10] Ayyapazham R. and Velautham K., “Proficient Decision Making on Virtual Machine Creation in Iaas Cloud Environment,” The International Arab Journal of Information Technology, vol. 14, no. 3, pp. 314-323, 2017.

[11] Boukerche A., Guan S., and Grande R., “Sustainable Offloading in Mobile Cloud Computing: Algorithmic Design and Implementation,” ACM Computing Surveys, vol. 52, no. 1, pp. 1-37, 2019.

[12] Byrne B., “Strategies in Testing for An Invariant Second‐Order Factor Structure: A Comparison of EQS and LISREL,” Structural Equation Modeling: A Multidisciplinary Journal, vol. 2, no. 1, pp. 53-72, 1995.

[13] Byrne B., Shavelson R., and Muthen B., “Testing for the Equivalence of Factor Covariance and Mean Structures: The Issue of Partial Measurement in Variance,” Psychological Bulletin, vol. 105, no. 3, pp. 456-466, 1989.

[14] Crawford J., Butler-Henderson K., Rudolph J., Malkawi B., Glowatz M., Burton R., Magni P., and Lam S., “COVID-19: 20 Countries' Higher Education Intra-Period Digital Pedagogy Responses,” Journal of Applied Learning and Teaching, vol. 3, no. 1, pp. 1-20, 2020.

[15] De Donno M., Tange K., and Dragoni N., “Foundations and Evolution of Modern Computing Paradigms: Cloud, Iot, Edge, and Fog,” IEEE Access, vol. 7, pp. 150936-150948, 2019.

[16] Dwivedi Y., Rana N., Jeyaraj A., Clement M., and Williams M., “Re-Examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards A Revised Theoretical Model,” Information Systems Frontiers, vol. 21, no. 3, pp. 719-734, 2019.

[17] Hair J., Black W., Babin B., Anderson R., and Tatham R., Multivariate Data Analysis, Pearson Prentice Hall, 2010.

[18] Hussein L. and Hilmi M., “Cloud Computing Based E-Learning in Malaysian Universities,” International Journal of Emerging Technologies in Learning, vol. 15, no. 08, pp. 4-21, 2020.

[19] Ikram M., “AI-driven Service Broker for Simple and Composite Cloud SaaS Selection,” Doctoral Dissertation, University of Technology Sydney, 2020.

[20] Islam A., “Viability of the Extended Technology Acceptance Model: An Empirical Study,” Journal of Information and Communication Technology, vol. 10, no. 1, pp. 85-98, 2011.

[21] Jaber O., “An Examination of Variables Influencing The Acceptance and Usage of E- Learning Systems in Jordanian Higher Education Institutions,” Doctoral Dissertation, Cardiff Metropolitan University, 2016.

[22] Jaradat M., Ababneh H., Faqih K., and Nusairat N., “Exploring Cloud Computing Adoption in Higher Educational Environment: An Extension of the UTAUT Model with Trust,” International Journal of Advanced Science and Technology vol. 29, no. 5, pp. 8282-8306, 2020.

[23] Khechine H., Raymond B., and Augier M., “The Adoption of A Social Learning System: Intrinsic Value in The UTAUT Model,” British Journal of Educational Technology, vol. 51, no. 6, pp. 2306- 2325, 2020.

[24] Marinković V. and Kalinić Z., Understanding “Consumers' Continuance Intention and Word of Mouth in Mobile Commerce Based on Extended UTAUT Model,” Impact of Mobile Services on Business Development and E-Commerce, pp. 108-125, 2020.

[25] Montaño D. and Kasprzyk D., “Theory of Reasoned Action, Theory of Planned Behavior, and the Integrated Behavioral Model,” Health Behavior and Health Education: Theory, Research, and Practice, Jossey-Bass, pp. 95-124, 2015.

[26] Mora H., Gimeno F., Signes-Pont M., and Volckaert B., “Multilayer Architecture Model for Mobile Cloud Computing Paradigm,” Complexity, vol. 2019, 2019.

[27] Muhairat M., Abdallah M., and Althunibat A., “Cloud Computing in Higher Educational Institutions,” Compusoft, vol. 8, no. 12, pp. 3507- A Multi-Group Structural Equation Modeling For Assessing Behavioral Intention of Using … 213 3513, 2019.

[28] Ouahabi S., Eddaoui A., Labriji E., Benlahmar, E., and El Guemmat K., “Implementation of a Novel Educational Modeling Approach for Cloud Computing,” International Journal of Emerging Technologies in Learning, vol. 9, no. 6, pp. 49- 53, 2014.

[29] Rababah K., Al-nassar B., Alnsour S., “Factors Influencing the Adoption of Cloud Computing in Small and Medium Enterprises in Jordan,” International Journal of Cloud Applications and Computing, vol. 10, no. 3, pp. 96-110, 2020.

[30] Ruangvanich S. and Piriyasurawong P., “Structural Equation Model of Acceptance Cloud Learning for Sustainability Usage in Higher Education Institutes,” International Journal of Emerging Technologies in Learning, vol. 14, no. 10, pp. 18-33, 2019.

[31] Sandu N. and Gide E., “Analysis of the Main Factors Affecting the Adoption of Cloud based Interactive Mobile Learning in the Australian Higher Education Sector,” International Journal of Interactive Mobile Technologies, vol. 12, no. 4, pp. 43-58, 2018.

[32] Singh P., “Mobile+Cloud: Opportunities and Challenges,” Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, pp. 784- 804, 2020.

[33] Taylor S. and Todd P., “Understanding Information Technology Usage: A Test of Competing Models,” Information Systems Research. vol. 6, no. 2, pp. 144-176, 1995.

[34] Taylor S. and Todd P., “Assessing IT usage: The Role of Prior Experience,” MIS Quarterly, vol. 19, no. 4, pp. 561-570, 1995.

[35] Tripathy S. and Jyotishi A., “Macro Factors Affecting Cloud Computing Readiness: A Cross- Country Analysis,” in Proceedings of Advances in Data Sciences, Security and Applications, Singapore, pp. 197-209, 2020.

[36] Venkatesh V., Morris M., Davis G., and Davis F., “User Acceptance of Information Technology: Toward A Unified View,” MIS Quarterly, vol. 27, no. 3, pp. 425-478, 2003.

[37] Yassein M., Hmeidi I., Alsmadi A., and Shatnawi M., “Cloud Computing Role in Internet of Things: Business Community Survey,” in Proceedings of 11th International Conference on Information and Communication Systems, Irbid, pp. 343-348, 2020.