CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE)

Shaden Masadeh; Rabab Abumalloh; Noha Labanı

doi:10.17718/tojde.1080016

EN

CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE)

Abstract

The novel COVID-19 pandemic has impacted educational systems in almost all countries worldwide. Traditional classes have been canceled or shifted to online mode through the affected countries. Resuming traditional face-to-face instruction might be delayed. This unexpectedly fast and mandatory shifting to online education, along with the significant challenges that face learners and instructors, has led to uncertainty regarding its future. This study aims to inspect students’ continuous intention (CI) towards online education during COVID-19, by incorporating different constructs from three theoretical models: first, conservation values( Security(SEC), Conformity(CON)) of Schwartz Value Theory(SVT), organizational support factors (Training(TR), Top management support(TS)) in Technology-Organizational-Environmental (TOE) , and the Technology acceptance model(TAM ) main factors (perceived usefulness(PU), perceived ease of use(PEU)). To achieve the research goal, a research model was developed referring to previous strong literature. The data was gathered from 310 students from Imam Abdulrahman Bin Faisal university (IAU) in Saudi Arabia, and analyzed with Structural Equation Modelling SEM-PLS. Findings show that TAM factors (PU, PEU), conservation values factors (SEC, CON), and organizational support factors (TR, TS) are important determinants for online learning adoption during COVID19 pandemic . The study provides directions for designers and developers to establish a more effective online learning environment, which is more suited for the new digitized generation during unexpected conditions.

Keywords

Online learning, Schwartz’s Value Theory, COVID-19, technology-organizationalenvironmental, TAM, continuous intention

Supporting Institution

IMAM ABDULRAHMAN BIN FAISAL UNIVERSITY

Project Number

NO NUMBER

Thanks

THANKS TO ALL WHO WORK IN SURVEY AND HELP UN THIS WORK

References

1. Agasisti, T., & Soncin, M. (2021). Studies in Higher Education Higher education in troubled times : on the impact of Covid-19 in Italy. https://doi.org/10.1080/03075079.2020.1859689
2. Ahmad, W., & Sun, J. (2018). Antecedents of SMMA continuance intention in two culturally diverse countries : An empirical examination. Journal of Global Information Technology Management, 21(1), 45–68. https://doi.org/10.1080/1097198X.2018.1423840
3. Al-hawari, M. A., & Mouakket, S. (2010). The influence of technology acceptance model (TAM) factors on students’ e-satisfaction and e-retention within the context of UAE e-learning. Education, Business and Society: Contemporary Middle Eastern Issues, 3(4), 299–314. https://doi.org/10.1108/17537981011089596
4. Aldikanji, E., & Ajami, K. (2016). Studying Academic Indicators within Virtual Learning Environment Using Educational Data Mining. International Journal of Data Mining & Knowledge Management Process, 6(6), 29–42. https://doi.org/10.5121/ijdkp.2016.6603
5. Alqahtani, F. N. (2016). Identifying the Critical Factors that Impact on the Development of Electronic Government using TOE Framework in Saudi E-Government Context: A Thematic Analysis. PQDT - UK & Ireland, October, 270.
6. Alves, P., Miranda, L., & Morais, C. (2017). The Influence of Virtual Learning Environments in Students’ Performance. Universal Journal of Educational Research, 5(3), 517–527. https://doi.org/10.13189/ujer.2017.050325
7. Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information and Management, 41(6), 731–745. https://doi.org/10.1016/j.im.2003.08.010
8. Arpaci, I. (2017). Antecedents and consequences of cloud computing adoption in education to achieve knowledge management. Computers in Human Behavior, 70, 382–390. https://doi.org/10.1016/j.chb.2017.01.024
9. Asoodar, M., Vaezi, S., & Izanloo, B. (2016). Framework to improve e-learner satisfaction and further strengthen e-learning implementation. Computers in Human Behavior, 63, 704–716. https://doi.org/10.1016/j.chb.2016.05.060
10. Awa, H. O., Ojiabo, O. U., & Emecheta, B. C. (2015). Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science & Technology Policy Management, 6(1), 76–94. https://doi.org/10.1108/JSTPM-04-2014-0012

11. Babu, S. C., Ferguson, J., Parsai, N., & Almoguera, R. (2013). Open distance learning for development: Lessons from strengthening research capacity on gender, crisis prevention, and recovery. International Review of Research in Open and Distance Learning, 14(5), 27–50. https://doi.org/10.19173/irrodl.v14i5.1611
12. Bagchi, K. K., Udo, G. J., Kirs, P. J., & Choden, K. (2015). Internet use and human values: Analyses of developing and developed countries. Computers in Human Behavior, 50, 76–90. https://doi.org/10.1016/j.chb.2015.03.055
13. Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024
14. Binyamin, S. S., Rutter, M. J., & 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, 14(3), 4–21. https://doi.org/10.3991/ijet.v14i03.9732
15. Boateng, R., Mbrokoh, A. S., Boateng, L., Senyo, P. K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. International Journal of Information and Learning Technology, 33(4), 248–262. https://doi.org/10.1108/IJILT-02-2016-0008
16. Boer, D., & Fischer, R. (2013). How and when do personal values guide our attitudes and saociality? Explaining cross-cultural variability in attitude–value linkages. Psychological Bulletin, 139(5), 1113.
17. Borgman, H. P., Bahli, B., Heier, H., & Schewski, F. (2013). Cloudrise: Exploring cloud computing adoption and governance with the TOE framework. Proceedings of the Annual Hawaii International Conference on System Sciences, 4425–4435. https://doi.org/10.1109/HICSS.2013.132
18. C, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065
19. Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83. https://doi.org/10.1111/j.1467-8535.2007.00742.x
20. Chau, P. Y. K., & Hu, P. J. H. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information and Management, 39(4), 297–311. https://doi.org/10.1016/S0378-7206(01)00098-2
21. Chau, P. Y. K., & Tam, K. Y. (1997). Factors affecting the adoption of open systems: An exploratory study. MIS Quarterly: Management Information Systems, 21(1), 1–20. https://doi.org/10.2307/249740
22. Cheng, B., Wang, M., Yang, S. J. H., Kinshuk, & Peng, J. (2011). Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support. Computers and Education, 57(1), 1317–1333. https://doi.org/10.1016/j.compedu.2011.01.018
23. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers and Education, 63, 160–175. https://doi.org/10.1016/j.compedu.2012.12.003
24. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge.
25. Davidov, E., Schmidt, P., & Schwartz, S. H. (2008). Bringing values back in: The adequacy of the European Social Survey to measure values in 20 countries. Public Opinion Quarterly, 72(3), 420–445.
26. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
27. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
28. Diddi, S., & Niehm, L. S. (2017). Exploring the role of values and norms towards consumers ’ intentions to patronize retail apparel brands engaged in corporate social responsibility ( CSR ). Fashion and Textiles. https://doi.org/10.1186/s40691-017-0086-0
29. Eseroghene, U., & Ahmad, A. (2018). The Impact of E-Learning on Academic Performance: Preliminary Examination of King Khalid University. International Journal of Academic Research in Progressive Education and Development, 7(71), 83–96. https://doi.org/10.6007/IJARPED/v7-i1/3903
30. Freitas, S. De, Oliver, M., Freitas, S. De, & Oliver, M. (2006). Does E ‐ learning Policy Drive Change in Higher Education ?: A case study relating models of organisational change to e ‐ learning implementation Does E-learning Policy Drive Change in Higher Education ?: A case study relating models of organisational cha. 9508, 80–95. https://doi.org/10.1080/13600800500046255
31. Friedrich-Baasner, G., Fischer, M., & Winkelmann, A. (2018). Cloud Computing in SMEs: A Qualitative Approach to Identify and Evaluate Influential Factors. Proceedings of the 51st Hawaii International Conference on System Sciences, 9, 4681–4690. https://doi.org/10.24251/hicss.2018.590
32. Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065
33. Gao, Q., Hu, Y., Dai, Z., Xiao, F., Wang, J., & Wu, J. (2020). The Epidemiological Characteristics of 2019 Novel Coronavirus Diseases (COVID-19) in Jingmen, China. SSRN Electronic Journal, 2(8), 113–122. https://doi.org/10.2139/ssrn.3548755
34. Garay, L. (2019). Heliyon Analysis of the third-order structuring of Shalom Schwartz ’ s theory of basic human values n. 5(November 2018), 1–7. https://doi.org/10.1016/j.heliyon.2019.e01797
35. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and tam in online shopping: AN integrated model. MIS Quarterly: Management Information Systems, 27(1), 51–90.
36. Goncalves, G., Oliveira, T., & Cruz-Jesus, F. (2018). Understanding individual-level digital divide: Evidence of an African country. Computers in Human Behavior, 87(March), 276–291. https://doi.org/10.1016/j.chb.2018.05.039
37. Goyal, G., Phukan, A. C., Hussain, M., Lal, V., Modi, M., Goyal, M. K., & Sehgal, R. (2019). Correlation Between Weather and Covid-19 Pandemic in Jakarta, Indonesia. Journal of the Neurological Sciences, 116544. https://doi.org/10.1016/j.jns.2019.116544
38. Grigoryan, L. K., Lebedeva, N., & Breugelmans, S. M. (2018a). A Cross-Cultural Study of the Mediating Role of Implicit Theories of Innovativeness in the Relationship Between Values and Attitudes Toward Innovation. Journal of Cross-Cultural Psychology, 49(2), 336–352. https://doi.org/10.1177/0022022116656399
39. Grigoryan, L. K., Lebedeva, N., & Breugelmans, S. M. (2018b). A Cross-Cultural Study of the Mediating Role of Implicit Theories of Innovativeness in the Relationship Between Values and Attitudes Toward Innovation. https://doi.org/10.1177/0022022116656399 40. Gülbahar, Y. (2007). Technology planning: A roadmap to successful technology integration in schools. Computers and Education, 49(4), 943–956. https://doi.org/10.1016/j.compedu.2005.12.002
41. Hair, J., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling. In Sage publications (Vol. 46, Issues 1–2). SAGE Publications Inc. https://doi.org/10.1016/j.lrp.2013.01.002
42. Icek, A. (1991). The Theory of Planned Behavior Organizational Behavior and Human Decision Processes. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
43. Igbaria, M., & Angele, L. M. (1997). Personal computing acceptance factors in small firms : A structural equation model.
44. January, S. (2020). Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 Resource Centre Is Hosted on Elsevier Connect, the Company’s Public News and Information.
45. Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21(1), 1–23. https://doi.org/10.1057/palgrave.jit.2000056
46. Journal, S., Support, T., & Park, S. Y. (2009). International Forum of Educational Technology & Society An Analysis of the Technology Acceptance Model in Understanding University Students ’ Behavioral Intention to Use e-Learning Author ( s ): Sung Youl Park Published by : International Forum of Educati. 12(3).
47. Keil, M., Beranek, P. M., & Konsynski, B. R. (1995). Usefulness and ease of use: field study evidence regarding task considerations. Decision Support Systems, 13(1), 75–91. https://doi.org/10.1016/0167-9236(94)E0032-M
48. Kerimoglu, O., Basoglu, N., & Daim, T. (2008). Organizational adoption of information technologies: Case of enterprise resource planning systems. Journal of High Technology Management Research, 19(1), 21–35. https://doi.org/10.1016/j.hitech.2008.06.002
49. Khachfe, H. H., Chahrour, M., Sammouri, J., Salhab, H. A., Makki, B. E., & Fares, M. Y. (2020). An Epidemiological Study on COVID-19: A Rapidly Spreading Disease. Cureus, March. https://doi.org/10.7759/cureus.7313
50. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.
51. Konradt, U., Christophersen, T., & Schaeffer-Kuelz, U. (2006). Predicting user satisfaction, strain and system usage of employee self-services. International Journal of Human Computer Studies, 64(11), 1141–1153. https://doi.org/10.1016/j.ijhcs.2006.07.001
52. Kummer, T. F., Recker, J., & Bick, M. (2017). Technology-induced anxiety: Manifestations, cultural influences, and its effect on the adoption of sensor-based technology in German and Australian hospitals. Information and Management, 54(1), 73–89. https://doi.org/10.1016/j.im.2016.04.002
53. Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Educational Technology and Society, 14(4), 124–137.
54. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4
55. Lian, J. W., Yen, D. C., & Wang, Y. T. (2014). An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal of Information Management, 34(1), 28–36. https://doi.org/10.1016/j.ijinfomgt.2013.09.004
56. Liang, Y., Qi, G., Wei, K., & Chen, J. (2017). Exploring the determinant and influence mechanism of e-Government cloud adoption in government agencies in China. Government Information Quarterly, 34(3), 481–495. https://doi.org/10.1016/j.giq.2017.06.002
57. Liaw, S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864–873. https://doi.org/10.1016/j.compedu.2007.09.005
58. Lisewski, B. (2004). Implementing a learning technology strategy : top – down strategy meets bottom – up culture. 12(2). https://doi.org/10.1080/0968776042000216228
59. Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers and Education, 54(2), 600–610. https://doi.org/10.1016/j.compedu.2009.09.009
60. Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management and Data Systems, 111(7), 1006–1023. https://doi.org/10.1108/02635571111161262
61. Marinoni, G., Van’t Land, H., & Jensen, T. (2020). The impact of Covid-19 on higher education around the world. IAU Global Survey Report.
62. Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The Influence of Values on E-learning Adoption. Computers & Education, 141(December 2018), 103617. https://doi.org/10.1016/j.compedu.2019.103617
63. Mohd Sharif, M. H., Rosli, K., & Ahmi, A. (2017). A Model of Social Media Adoption and Impact on Malaysian Small and Medium-sized Enterprises (SMEs). Proceedings of the 4th International Conference on E-Commerce (ICoEC) 2017, 148–152.
64. Molnar, A., Miron, G., Elgeberi, N., Barbour, M. K., Huerta, L., Shafer, S. R., & Rice, J. K. (2019). Virtual Schools in the U.S. 2019. 0249(May). 65. Mtingwi, M. (2015). E-Education adoption in emerging economy countries: Case of Malawi. 2015 IST-Africa Conference, IST-Africa 2015, 1–9. https://doi.org/10.1109/ISTAFRICA.2015.7190567
66. Oliveira, T., & Martins, M. F. (2009). Firms patterns of -business adoption: Evidence for the European union-27. Proceedings of the 3rd European Conference on Information Management and Evaluation, ECIME 2009, 13(1), 371–379.
67. Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829. https://doi.org/10.1016/j.chb.2004.03.006
68. Pahnila, S., Siponen, M., Myyry, L., & Zheng, X. (2011). the Influence of Individualistic and Collectivistic Values To Utaut: the Case of the Chinese Ebay. Ecis, 2011.
69. Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of literature. International Journal of Information Management, 43(July 2016), 1–14. https://doi.org/10.1016/j.ijinfomgt.2018.05.005
70. Partala, T., & Saari, T. (2015). Understanding the most influential user experiences in successful and unsuccessful technology adoptions. Computers in Human Behavior, 53, 381–395.
71. Polloff, R. M., & Pratt, K. (2001). Lessons from the cyberspace classroom. The Realities of Online Teaching. San Francisco: Jossey-Bass.
72. Price, L., Richardson, J. T. E., Jelfs, A., Price, L., Richardson, J. T. E., & Jelfs, A. (2007). Studies in Higher Education Face ‐ to ‐ face versus online tutoring support in distance education Face-to-face versus online tutoring support in distance education. 5079. https://doi.org/10.1080/03075070601004366
73. Purnomo, S. H., & Lee, Y. H. (2013). E-learning adoption in the banking workplace in Indonesia: An empirical study. Information Development, 29(2), 138–153. https://doi.org/10.1177/0266666912448258
74. Ramdani, B., Kawalek, P., & Lorenzo, O. (2009). Predicting SMEs’ adoption of enterprise systems. Journal of Enterprise Information Management, 22, 10–24. https://doi.org/10.1108/17410390910922796
75. Rashid, S., & Yadav, S. S. (2020). Impact of Covid-19 Pandemic on Higher Education and Research. 14(2), 340–343. https://doi.org/10.1177/0973703020946700
76. Rhema, A., & Miliszewska, I. (2012). The Potential of E-Learning in Assisting Post-Crisis Countries in Re-Building Their Higher Education Systems: The Case of Libya. Issues in Informing Science and Information Technology, 9(January 2012), 149–160. https://doi.org/10.28945/1611
77. Riyadh, A. N., Akter, S., & Islam, N. (2009). The Adoption of E-banking in Developing Countries : A Theoretical Model for SMEs. International Review of Business Research Papers, 5(6), 212–230. https://doi.org/10.1016/j.technovation.2007.10.003
78. Rizun, M., & Strzelecki, A. (2020). Students’ Acceptance of the COVID-19 Impact on Shifting Higher Education to Distance Learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 6468. https://doi.org/10.3390/ijerph17186468 79. Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
80. Rokeach, M. (1973). The nature of human values (new editio). New York: The Free Press, Macmillan Publishing Co. Inc.
81. Salehan, M., Kim, D. J., & Lee, J. N. (2018). Are there any relationships between technology and cultural values? A country-level trend study of the association between information communication technology and cultural values. Information and Management, 55(6), 725–745. https://doi.org/10.1016/j.im.2018.03.003
82. Salwani, M. I., Marthandan, G., Norzaidi, M. D., & Chong, S. C. (2009). E-commerce usage and business performance in the Malaysian tourism sector: Empirical analysis. Information Management and Computer Security, 17(2), 166–185. https://doi.org/10.1108/09685220910964027
83. Sánchez, R. A., Hueros, A. D., & Ordaz, M. G. (2013). E-learning and the University of Huelva: A study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135–160. https://doi.org/10.1108/10650741311306318
84. Saqr, M., Fors, U., & Tedre, M. (2018). How the study of online collaborative learning can guide teachers and predict students’ performance in a medical course. BMC Medical Education, 18(1), 1–14. https://doi.org/10.1186/s12909-018-1126-1
85. Saris, W. E., & Schwartz, S. H. (2013). Operationalizing the Theory of Human Values : Balancing Homogeneity of Reflective Items and Theoretical Coverage. 7(1), 29–44.
86. Schillewaert, N., Ahearne, M. J., Frambach, R. T., & Moenaert, R. K. (2005). The adoption of information technology in the sales force. Industrial Marketing Management, 34(4 SPEC ISS.), 323–336. https://doi.org/10.1016/j.indmarman.2004.09.013
87. Schwartz, S. H. (1992). Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. Advances in Experimental Social Psychology, 25(1), 1–65.
88. Schwartz, S. H. (1994). Are There Universal Aspects in the Structure and Contents of Human Values? Journal of Social Issues, 50(4), 19–45. https://doi.org/10.1111/j.1540-4560.1994.tb01196.x
89. Schwartz, S. H. (2012). A Proposal for Measuring Value Orientations across Nations. Core ESS Questionnaire, 259–319. https://doi.org/10.1111/j.1540-6237.2011.00830.x.Fitting
90. Schwartz, S. H., Cieciuch, J., Vecchione, M., Davidov, E., Fischer, R., Beierlein, C., Ramos, A., Verkasalo, M., Lönnqvist, J. E., Demirutku, K., Dirilen-Gumus, O., & Konty, M. (2012). Refining the theory of basic individual values. Journal of Personality and Social Psychology, 103(4), 663–688. https://doi.org/10.1037/a0029393
91. Seddig, D., & Davidov, E. (2018). Values , Attitudes Toward Interpersonal Violence , and Interpersonal Violent Behavior. 9(May), 1–13. https://doi.org/10.3389/fpsyg.2018.00604
92. Senyo, P. K., Effah, J., & Addae, E. (2016). Preliminary insight into cloud computing adoption in a developing country. Journal of Enterprise Information Management, 29(4), 505–524. https://doi.org/10.1108/JEIM-09-2014-0094
93. Shahzad, F., Xiu, G. Y., Khan, I., Shahbaz, M., Riaz, M. U., & Abbas, A. (2020). The moderating role of intrinsic motivation in cloud computing adoption in online education in a developing country: a structural equation model. Asia Pacific Education Review, 21(1), 121–141. https://doi.org/10.1007/s12564-019-09611-2
94. Shih, H. P. (2004). Extended technology acceptance model of Internet utilization behavior. Information and Management, 41(6), 719–729. https://doi.org/10.1016/j.im.2003.08.009
95. Singh, G., & Hardaker, G. (2014). Barriers and enablers to adoption and diffusion of eLearning : A systematic review of the literature - a need for an integrative approach. Education and Training, 56(2), 105–121. https://doi.org/10.1108/ET-11-2012-0123
96. Singh, R. K. (2013). Analyzing the Factors for VMI Implementation: A Framework. Global Business Review, 14(1), 169–186. https://doi.org/10.1177/0972150912466476
97. Smith, P. B. (2002). Levels of Analysis in Cross-Cultural Psychology. Online Readings in Psychology and Culture, 2(2), 1–9. https://doi.org/10.9707/2307-0919.1018
98. Srite, M., & Karahanna, E. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly: Management Information Systems, 30(3), 679–704. https://doi.org/10.2307/25148745
99. Surry, D. W., Ensminger, D. C., & Haab, M. (2005). A model for integrating instructional technology into higher education. British Journal of Educational Technology, 36(2), 327–329.
100. Tantiponganant, P., & Laksitamas, P. (2014). An analysis of the technology acceptance model in understanding students’ behavioral intention to use university’s social media. Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014, 12, 8–12. https://doi.org/10.1109/IIAI-AAI.2014.14
101. Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328. https://doi.org/10.1080/10494820.2015.1122635
102. Taylor, S., & Todd, P. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19(4), 561. https://doi.org/10.2307/249633
103. Teo, T. S. H., Lin, S., & Lai, K. hung. (2009). Adopters and non-adopters of e-procurement in Singapore: An empirical study. Omega, 37(5), 972–987. https://doi.org/10.1016/j.omega.2008.11.001
104. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington books.
105. Tweel, A. (2012). Examining the Relationship between Technological , Organizational , and Environmental Factors and Cloud Computing Adoption Dissertation Submitted to Northcentral University Graduate Faculty of the School of Business and Technology Management in Partial Fu. ProQuest LLC, July, 164.
106. Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2012). Exploring the role of espoused values on e-service adoption: A comparative analysis of the US and Nigerian users. Computers in Human Behavior, 28(5), 1768–1781. https://doi.org/10.1016/j.chb.2012.04.017
107. van de Heyde, V., & Siebrits, A. (2019). The ecosystem of e-learning model for higher education. South African Journal of Science, 115(5–6), 78–84. https://doi.org/10.17159/sajs.2019/5808
108. Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly: Management Information Systems, 23(2), 239–260. https://doi.org/10.2307/249753
109. Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
110. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540
111. Wang, S., & Noe, R. A. (2010). Human Resource Management Review Knowledge sharing : A review and directions for future research. Human Resource Management Review, 20(2), 115–131. https://doi.org/10.1016/j.hrmr.2009.10.001
112. Wang, Y. M., Wang, Y. S., & Yang, Y. F. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77(5), 803–815. https://doi.org/10.1016/j.techfore.2010.03.006
113. Weltman, H. R., Timchenko, V., Sofios, H. E., Ayres, P., & Marcus, N. (2019). Evaluation of an adaptive tutorial supporting the teaching of mathematics. European Journal of Engineering Education, 44(5), 787–804. https://doi.org/10.1080/03043797.2018.1513993
114. Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001
115. Xie, M., & Chen, Q. (2020). Insight into 2019 novel coronavirus — An updated interim review and lessons from SARS-CoV and MERS-CoV. International Journal of Infectious Diseases, 94, 119–124. https://doi.org/10.1016/j.ijid.2020.03.071
116. Yilmaz, O. (2015). The effects of “live virtual classroom” on students’ achievement and students’ opinions about “live virtual classroom” at distance education. Turkish Online Journal of Educational Technology, 14(1), 108–115.
117. Zhu, K. (2004). The complementarity of information technology infrastructure and E-commerce capability: A Resource-based assessment of their business value. Journal of Management Information Systems, 21(1), 167–202. https://doi.org/10.1080/07421222.2004.11045794

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Shaden Masadeh ^*
0000-0002-9256-5900
Saudi Arabia

Rabab Abumalloh This is me
0000-0003-2805-3764
Saudi Arabia

Noha Labanı This is me
0009-0006-7283-0195
Saudi Arabia

Publication Date

April 1, 2023

Submission Date

February 27, 2022

Acceptance Date

June 6, 2022

Published in Issue

Year 2023 Volume: 24 Number: 2

DOI

https://doi.org/10.17718/tojde.1080016

IZ

https://izlik.org/JA63JR29KH

APA

Masadeh, S., Abumalloh, R., & Labanı, N. (2023). CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE). Turkish Online Journal of Distance Education, 24(2), 284-307. https://doi.org/10.17718/tojde.1080016

AMA

1.Masadeh S, Abumalloh R, Labanı N. CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE). TOJDE. 2023;24(2):284-307. doi:10.17718/tojde.1080016

Chicago

Masadeh, Shaden, Rabab Abumalloh, and Noha Labanı. 2023. “CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE)”. Turkish Online Journal of Distance Education 24 (2): 284-307. https://doi.org/10.17718/tojde.1080016.

EndNote

Masadeh S, Abumalloh R, Labanı N (April 1, 2023) CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE). Turkish Online Journal of Distance Education 24 2 284–307.

IEEE

[1]S. Masadeh, R. Abumalloh, and N. Labanı, “CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE)”, TOJDE, vol. 24, no. 2, pp. 284–307, Apr. 2023, doi: 10.17718/tojde.1080016.

ISNAD

Masadeh, Shaden - Abumalloh, Rabab - Labanı, Noha. “CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE)”. Turkish Online Journal of Distance Education 24/2 (April 1, 2023): 284-307. https://doi.org/10.17718/tojde.1080016.

JAMA

1.Masadeh S, Abumalloh R, Labanı N. CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE). TOJDE. 2023;24:284–307.

MLA

Masadeh, Shaden, et al. “CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE)”. Turkish Online Journal of Distance Education, vol. 24, no. 2, Apr. 2023, pp. 284-07, doi:10.17718/tojde.1080016.

Vancouver

1.Shaden Masadeh, Rabab Abumalloh, Noha Labanı. CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE). TOJDE. 2023 Apr. 1;24(2):284-307. doi:10.17718/tojde.1080016

Cited By

Fen Derslerinde Kullanılan Uzaktan Eğitim Platformlarına Yönelik Öğrenci Tutumlarının Belirlenmesi

Uluslararası Karamanoğlu Mehmetbey Eğitim Araştırmaları Dergisi

https://doi.org/10.47770/ukmead.1394168