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IMPACT OF COVID-19 PANDEMIC ON ACCEPTANCE OF E-LEARNING SYSTEM IN JORDAN: A CASE OF TRANSFORMING THE TRADITIONAL EDUCATION SYSTEMS
Corresponding Author(s) : Manaf Al-Okaily
Humanities & Social Sciences Reviews,
Vol. 8 No. 4 (2020): July
Abstract
Purpose of the study: This paper aimed at investigating factors influencing students’ intention to use e-learning within the context of Jordan. The proposed model integrated subjective norms with the extended Technology Acceptance Model (TAM).
Methodology: The data collected 587 students from Jadara University. Data were analyzed using Partial Least Squares - Structural Equation Modelling (PLS-SEM).
Main Findings: The result has confirmed the direct effect of all variables. Next, the result has also shown that the mediation effect of perceived usefulness and perceived ease of use between subjective norm and the behavioral intention to use the E-learning system was partially supported.
Applications of this study: The results of the current work may contribute to the improvement of the existing literature in the e-learning acceptance fields, and can be applied as an essential input for the development of the recent e-learning training programs.
Novelty/Originality of this study: The present work is intended to conclude a series of concrete research data as well as to contribute a new angle to the existing literature on students’ perception of the e-learning system.
Keywords
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- Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036 DOI: https://doi.org/10.1016/j.chb.2015.11.036
- Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T DOI: https://doi.org/10.1016/0749-5978(91)90020-T
- Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile Internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100-110. https://doi.org/10.1016/j.techsoc.2018.06.007 DOI: https://doi.org/10.1016/j.techsoc.2018.06.007
- Al-Fraihat, D., Joy, M., Masa’ deh. R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004 DOI: https://doi.org/10.1016/j.chb.2019.08.004
- Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001 DOI: https://doi.org/10.1016/j.aci.2014.09.001
- Alghizzawi, M., Habes, M., Salloum, S. A., Ghani, M. A., Mhamdi, C., & Shaalan, K. (2019). The effect of social media usage on students’ e-learning acceptance in higher education: A case study from the United Arab Emirates. International Journal of Information Technology and Language Studies, 3(3).
- Aljazzaf, Z. M., Al-Ali, S. A., & Sarfraz, M. (2020). e-Participation Model for Kuwait e-Government. People. International Journal of Advanced Computer Science and Applications, 11(2). https://doi.org/10.14569/IJACSA.2020.0110226 DOI: https://doi.org/10.14569/IJACSA.2020.0110226
- Almarabeh, T., Mohammad, H., Yousef, R., & Majdalawi, Y. K. (2014). The University of Jordan e-learning platform: State, students’ acceptance and challenges. Journal of Software Engineering and Applications, 7(12), 999.https://doi.org/10.4236/jsea.2014.712087 DOI: https://doi.org/10.4236/jsea.2014.712087
- Al-Okaily, A., Al-Okaily, M., Shiyyab, F., & Masadah, W. (2020). Accounting information system effectiveness from an organizational perspective. Management Science Letters, 10(16), 3991-4000. https://doi.org/10.5267/j.msl.2020.7.010 DOI: https://doi.org/10.5267/j.msl.2020.7.010
- Al-Okaily, M. M., & Rahman, M. S. A. (2017). The Impact of Implementing Web Trust Principles on the Efficiency of Accounting Information System in Commercial Banks at Jordan. Journal of Business and Management, 19 (7), 71-80.
- Al-Okaily, M., Abd Rahman, M. S., & Ali, A. (2019). Factors Affecting the Acceptance of Mobile Payment Systems in Jordan: The Moderating Role of Trust. Journal of Information System and Technology Management, 4(15), 16-26.
- https://doi.org/10.35631/jistm.415002 DOI: https://doi.org/10.35631/jistm.415002
- Alqudah, H. M., Amran, N. A., & Hassan, H. (2019a). Extrinsic Factors Influencing Internal Auditors’ Effectiveness in Jordanian Public Sector. Review of European Studies, 11(2), 67-79.
- https://doi.org/10.5539/res.v11n2p67 DOI: https://doi.org/10.5539/res.v11n2p67
- Alqudah, H., Amran, N. & Hassan, H. (2019b). Factors affecting the internal auditors’ effectiveness in the Jordanian public sector: The moderating effect of task complexity. EuroMed Journal of Business, 14(3), 251-273. https://doi.org/10.1108/EMJB-03-2019-0049 DOI: https://doi.org/10.1108/EMJB-03-2019-0049
- Alrawashdeh, T. (2011). The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector’s Employees in Jordan (Doctor dissertation). Universiti Utara Malaysia, Othman Yeop Abdullah Graduate School of Business, Sintok, Malaysia.
- Alsaad, A., Mohamad, R., & Ismail, N. A. (2015). Perceived desirability and firm’s intention to adopt business to business e-commerce: A test of second-order construct. Advanced Science Letters, 21(6), 2028-2032.
- https://doi.org/10.1166/asl.2015.6194 DOI: https://doi.org/10.1166/asl.2015.6194
- Binyamin, S. S., Rutter, M., & Smith, S. (2019). Extending the technology acceptance model to understand students’ use of learning management systems in Saudi higher education. International Journal of Emerging Technologies in Learning (iJET), 14(03), 4-21.†https://doi.org/10.3991/ijet.v14i03.9732 DOI: https://doi.org/10.3991/ijet.v14i03.9732
- Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9-54. https://doi.org/10.2753/MIS0742-1222270201 DOI: https://doi.org/10.2753/MIS0742-1222270201
- Chu, T. H., & Chen, Y. Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92(1), 37-52. M https://doi.org/10.1016/j.compedu.2015.09.013 DOI: https://doi.org/10.1016/j.compedu.2015.09.013
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Academic press. comparison of two theoretical models. Management science, 35(8), 982-1003.
- Davis, F. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
- El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763. https://doi.org/10.1007/s11423-016-9508-8 DOI: https://doi.org/10.1007/s11423-016-9508-8
- Epignosis, L. L. C. (2014). E-learning concepts, trends, applications. California: Epignosis LLC, 5(6), 7.
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An Introduction to theory and research. Reading, MA: Addison-Wesley.
- Fornell, V., & Larcker, C. (1981). Evaluating structural equation models with observable variables and measurement error. Journal of Marketing, 18(1), 39–50. DOI: https://doi.org/10.1177/002224378101800104
- Gefen, D., Rigdon, E. E., & Straub, D. W. (2011). An Update and Extension to SEM Guidelines for Administrative and Social Science Research. MIS Quarterly, 35(2). https://doi.org/10.2307/23044042 DOI: https://doi.org/10.2307/23044042
- George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference 12.0 update. Needham Heights, MA, USA: Allyn & Bacon, Inc.
- Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.UK. https://doi.org/10.1108/EBR-10-2013-0128 DOI: https://doi.org/10.1108/EBR-10-2013-0128
- Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications. New York: John Wiley & Sons
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-151. https://doi.org/10.2753/MTP1069-6679190202 DOI: https://doi.org/10.2753/MTP1069-6679190202
- Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2020). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 5.
- Hoppe, G. & Breitner, M. (2004) Business Models for E-Learning. Multikonferenz Wirtschaftsinformatik, Essen, Germany.
- Hsieh, J. P. A., Rai, A., & Keil, M. (2008). Understanding digital inequality: Comparing continued use behavioral models of the socio-economically advantaged and disadvantaged. MIS Quarterly, 32(1), 97-126. https://doi.org/10.2307/25148830 DOI: https://doi.org/10.2307/25148830
- Lutfi, A. A., Idris, K. M., & Mohamad, R. (2017). AIS usage factors and impact among Jordanian SMEs: The moderating effect of environmental uncertainty. Journal of Advanced Research in Business and Management Studies, 6(1), 24-38.
- Lutfi, A. A., Idris, K. M., & Mohamad, R. (2016). The influence of technological, organizational and environmental factors on accounting information system usage among Jordanian small and medium-sized enterprises. International Journal of Economics and Financial Issues, 6(7S), 240-248.
- Malhotra, Y., & Galletta, D.F. (1999). Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation. In proceedings of the thirty- second annual Hawaii international conference on system sciences, 1, 14. https://doi.org/10.1109/HICSS.1999.772658 DOI: https://doi.org/10.1109/HICSS.1999.772658
- Mathieson, K., Peacock, E., & Chin, W. (2001). Extending the technology acceptance model: The influence of perceived user resources. Database for Advances in Information Systems, 32(3), 86-112. https://doi.org/10.1145/506724.506730 DOI: https://doi.org/10.1145/506724.506730
- Miller, M. D., Kelly, R. R., & Ken, J. C. (2003). Predictors of engagement and participation in an online course. Online Journal of Distance Learning Administration, 6(1). Retrieved from: http://www.westga.edu/~distance/ojdla/spring61/miller61.htm.
- Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374. https://doi.org/10.1016/j.chb.2014.07.044 DOI: https://doi.org/10.1016/j.chb.2014.07.044
- Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(2), 192-222. https://doi.org/10.1287/isre.2.3.192 DOI: https://doi.org/10.1287/isre.2.3.192
- Mousa, A. H., Mousa, S. H., Mousa, S. H., & Obaid, H. A. (2020, January). Advance Acceptance Status Model for E-learning Based on University Academics and Students. In IOP Conference Series: Materials Science and Engineering (Vol. 671, No. 1, p. 012031). IOP Publishing. https://doi.org/10.1088/1757-899X/671/1/012031 DOI: https://doi.org/10.1088/1757-899X/671/1/012031
- Raid, M. (2009). An evaluation of information systems success: A user perspective - the case of Jordan telecom group. Europoean Journal of Scientific Research, 37(2), 226-239.
- Salloum, S. A., & Al-Emran, M. (2018). Factors affecting the adoption of E-payment systems by university students: Extending the TAM with trust. International Journal of Electronic Business, 14(4), 371-390. https://doi.org/10.1504/IJEB.2018.098130 DOI: https://doi.org/10.1504/IJEB.2018.098130
- Shankar, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3_suppl), S72-S89. https://doi.org/10.1177/0972150918757870 DOI: https://doi.org/10.1177/0972150918757870
- Sharma, S. K., Sarrab, M., & Al-Shihi, H. (2017). Development and validation of Mobile learning acceptance measure. Interactive Learning Environments, 25(7), 847-858. https://doi.org/10.1080/10494820.2016.1224250 DOI: https://doi.org/10.1080/10494820.2016.1224250
- Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 1-12. https://doi.org/10.1177/2158244013503837 DOI: https://doi.org/10.1177/2158244013503837
- Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153-163. https://doi.org/10.1016/j.chb.2014.09.020 DOI: https://doi.org/10.1016/j.chb.2014.09.020
- Taylor, S., & Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144 DOI: https://doi.org/10.1287/isre.6.2.144
- Tosuntas, S. B., Karadag, B. E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the unified theory of acceptance and use of technology. Computers & Education, 81(2), 169-178. https://doi.org/10.1016/j.compedu.2014.10.009 DOI: https://doi.org/10.1016/j.compedu.2014.10.009
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x DOI: https://doi.org/10.1111/j.1540-5915.2008.00192.x
- Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management and Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
- Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412 DOI: https://doi.org/10.2307/41410412
- Wolk, M. (2007). Using the technology acceptance model for outcomes assessment in higher education. Information Systems Education Journal, 7(43), 86-112.
- Yi, M., Jackson, J., Park, J., & Probst, J. (2006). The use of unified theory of acceptance and use of technology to confer the behavioral model of 3G mobile telecommunication users. Journal of Statistics and Management Systems, 43(3), 350-363.
- Al-Emran, M., & Teo, T. (2019). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 1-16. https://doi.org/10.1007/s10639-019-10062-w DOI: https://doi.org/10.1007/s10639-019-10062-w
References
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036 DOI: https://doi.org/10.1016/j.chb.2015.11.036
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T DOI: https://doi.org/10.1016/0749-5978(91)90020-T
Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile Internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100-110. https://doi.org/10.1016/j.techsoc.2018.06.007 DOI: https://doi.org/10.1016/j.techsoc.2018.06.007
Al-Fraihat, D., Joy, M., Masa’ deh. R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004 DOI: https://doi.org/10.1016/j.chb.2019.08.004
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001 DOI: https://doi.org/10.1016/j.aci.2014.09.001
Alghizzawi, M., Habes, M., Salloum, S. A., Ghani, M. A., Mhamdi, C., & Shaalan, K. (2019). The effect of social media usage on students’ e-learning acceptance in higher education: A case study from the United Arab Emirates. International Journal of Information Technology and Language Studies, 3(3).
Aljazzaf, Z. M., Al-Ali, S. A., & Sarfraz, M. (2020). e-Participation Model for Kuwait e-Government. People. International Journal of Advanced Computer Science and Applications, 11(2). https://doi.org/10.14569/IJACSA.2020.0110226 DOI: https://doi.org/10.14569/IJACSA.2020.0110226
Almarabeh, T., Mohammad, H., Yousef, R., & Majdalawi, Y. K. (2014). The University of Jordan e-learning platform: State, students’ acceptance and challenges. Journal of Software Engineering and Applications, 7(12), 999.https://doi.org/10.4236/jsea.2014.712087 DOI: https://doi.org/10.4236/jsea.2014.712087
Al-Okaily, A., Al-Okaily, M., Shiyyab, F., & Masadah, W. (2020). Accounting information system effectiveness from an organizational perspective. Management Science Letters, 10(16), 3991-4000. https://doi.org/10.5267/j.msl.2020.7.010 DOI: https://doi.org/10.5267/j.msl.2020.7.010
Al-Okaily, M. M., & Rahman, M. S. A. (2017). The Impact of Implementing Web Trust Principles on the Efficiency of Accounting Information System in Commercial Banks at Jordan. Journal of Business and Management, 19 (7), 71-80.
Al-Okaily, M., Abd Rahman, M. S., & Ali, A. (2019). Factors Affecting the Acceptance of Mobile Payment Systems in Jordan: The Moderating Role of Trust. Journal of Information System and Technology Management, 4(15), 16-26.
https://doi.org/10.35631/jistm.415002 DOI: https://doi.org/10.35631/jistm.415002
Alqudah, H. M., Amran, N. A., & Hassan, H. (2019a). Extrinsic Factors Influencing Internal Auditors’ Effectiveness in Jordanian Public Sector. Review of European Studies, 11(2), 67-79.
https://doi.org/10.5539/res.v11n2p67 DOI: https://doi.org/10.5539/res.v11n2p67
Alqudah, H., Amran, N. & Hassan, H. (2019b). Factors affecting the internal auditors’ effectiveness in the Jordanian public sector: The moderating effect of task complexity. EuroMed Journal of Business, 14(3), 251-273. https://doi.org/10.1108/EMJB-03-2019-0049 DOI: https://doi.org/10.1108/EMJB-03-2019-0049
Alrawashdeh, T. (2011). The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector’s Employees in Jordan (Doctor dissertation). Universiti Utara Malaysia, Othman Yeop Abdullah Graduate School of Business, Sintok, Malaysia.
Alsaad, A., Mohamad, R., & Ismail, N. A. (2015). Perceived desirability and firm’s intention to adopt business to business e-commerce: A test of second-order construct. Advanced Science Letters, 21(6), 2028-2032.
https://doi.org/10.1166/asl.2015.6194 DOI: https://doi.org/10.1166/asl.2015.6194
Binyamin, S. S., Rutter, M., & Smith, S. (2019). Extending the technology acceptance model to understand students’ use of learning management systems in Saudi higher education. International Journal of Emerging Technologies in Learning (iJET), 14(03), 4-21.†https://doi.org/10.3991/ijet.v14i03.9732 DOI: https://doi.org/10.3991/ijet.v14i03.9732
Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9-54. https://doi.org/10.2753/MIS0742-1222270201 DOI: https://doi.org/10.2753/MIS0742-1222270201
Chu, T. H., & Chen, Y. Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92(1), 37-52. M https://doi.org/10.1016/j.compedu.2015.09.013 DOI: https://doi.org/10.1016/j.compedu.2015.09.013
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Academic press. comparison of two theoretical models. Management science, 35(8), 982-1003.
Davis, F. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763. https://doi.org/10.1007/s11423-016-9508-8 DOI: https://doi.org/10.1007/s11423-016-9508-8
Epignosis, L. L. C. (2014). E-learning concepts, trends, applications. California: Epignosis LLC, 5(6), 7.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An Introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, V., & Larcker, C. (1981). Evaluating structural equation models with observable variables and measurement error. Journal of Marketing, 18(1), 39–50. DOI: https://doi.org/10.1177/002224378101800104
Gefen, D., Rigdon, E. E., & Straub, D. W. (2011). An Update and Extension to SEM Guidelines for Administrative and Social Science Research. MIS Quarterly, 35(2). https://doi.org/10.2307/23044042 DOI: https://doi.org/10.2307/23044042
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference 12.0 update. Needham Heights, MA, USA: Allyn & Bacon, Inc.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.UK. https://doi.org/10.1108/EBR-10-2013-0128 DOI: https://doi.org/10.1108/EBR-10-2013-0128
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications. New York: John Wiley & Sons
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-151. https://doi.org/10.2753/MTP1069-6679190202 DOI: https://doi.org/10.2753/MTP1069-6679190202
Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2020). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 5.
Hoppe, G. & Breitner, M. (2004) Business Models for E-Learning. Multikonferenz Wirtschaftsinformatik, Essen, Germany.
Hsieh, J. P. A., Rai, A., & Keil, M. (2008). Understanding digital inequality: Comparing continued use behavioral models of the socio-economically advantaged and disadvantaged. MIS Quarterly, 32(1), 97-126. https://doi.org/10.2307/25148830 DOI: https://doi.org/10.2307/25148830
Lutfi, A. A., Idris, K. M., & Mohamad, R. (2017). AIS usage factors and impact among Jordanian SMEs: The moderating effect of environmental uncertainty. Journal of Advanced Research in Business and Management Studies, 6(1), 24-38.
Lutfi, A. A., Idris, K. M., & Mohamad, R. (2016). The influence of technological, organizational and environmental factors on accounting information system usage among Jordanian small and medium-sized enterprises. International Journal of Economics and Financial Issues, 6(7S), 240-248.
Malhotra, Y., & Galletta, D.F. (1999). Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation. In proceedings of the thirty- second annual Hawaii international conference on system sciences, 1, 14. https://doi.org/10.1109/HICSS.1999.772658 DOI: https://doi.org/10.1109/HICSS.1999.772658
Mathieson, K., Peacock, E., & Chin, W. (2001). Extending the technology acceptance model: The influence of perceived user resources. Database for Advances in Information Systems, 32(3), 86-112. https://doi.org/10.1145/506724.506730 DOI: https://doi.org/10.1145/506724.506730
Miller, M. D., Kelly, R. R., & Ken, J. C. (2003). Predictors of engagement and participation in an online course. Online Journal of Distance Learning Administration, 6(1). Retrieved from: http://www.westga.edu/~distance/ojdla/spring61/miller61.htm.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374. https://doi.org/10.1016/j.chb.2014.07.044 DOI: https://doi.org/10.1016/j.chb.2014.07.044
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(2), 192-222. https://doi.org/10.1287/isre.2.3.192 DOI: https://doi.org/10.1287/isre.2.3.192
Mousa, A. H., Mousa, S. H., Mousa, S. H., & Obaid, H. A. (2020, January). Advance Acceptance Status Model for E-learning Based on University Academics and Students. In IOP Conference Series: Materials Science and Engineering (Vol. 671, No. 1, p. 012031). IOP Publishing. https://doi.org/10.1088/1757-899X/671/1/012031 DOI: https://doi.org/10.1088/1757-899X/671/1/012031
Raid, M. (2009). An evaluation of information systems success: A user perspective - the case of Jordan telecom group. Europoean Journal of Scientific Research, 37(2), 226-239.
Salloum, S. A., & Al-Emran, M. (2018). Factors affecting the adoption of E-payment systems by university students: Extending the TAM with trust. International Journal of Electronic Business, 14(4), 371-390. https://doi.org/10.1504/IJEB.2018.098130 DOI: https://doi.org/10.1504/IJEB.2018.098130
Shankar, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3_suppl), S72-S89. https://doi.org/10.1177/0972150918757870 DOI: https://doi.org/10.1177/0972150918757870
Sharma, S. K., Sarrab, M., & Al-Shihi, H. (2017). Development and validation of Mobile learning acceptance measure. Interactive Learning Environments, 25(7), 847-858. https://doi.org/10.1080/10494820.2016.1224250 DOI: https://doi.org/10.1080/10494820.2016.1224250
Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 1-12. https://doi.org/10.1177/2158244013503837 DOI: https://doi.org/10.1177/2158244013503837
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153-163. https://doi.org/10.1016/j.chb.2014.09.020 DOI: https://doi.org/10.1016/j.chb.2014.09.020
Taylor, S., & Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144 DOI: https://doi.org/10.1287/isre.6.2.144
Tosuntas, S. B., Karadag, B. E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the unified theory of acceptance and use of technology. Computers & Education, 81(2), 169-178. https://doi.org/10.1016/j.compedu.2014.10.009 DOI: https://doi.org/10.1016/j.compedu.2014.10.009
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x DOI: https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management and Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412 DOI: https://doi.org/10.2307/41410412
Wolk, M. (2007). Using the technology acceptance model for outcomes assessment in higher education. Information Systems Education Journal, 7(43), 86-112.
Yi, M., Jackson, J., Park, J., & Probst, J. (2006). The use of unified theory of acceptance and use of technology to confer the behavioral model of 3G mobile telecommunication users. Journal of Statistics and Management Systems, 43(3), 350-363.
Al-Emran, M., & Teo, T. (2019). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 1-16. https://doi.org/10.1007/s10639-019-10062-w DOI: https://doi.org/10.1007/s10639-019-10062-w