International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

line decor
  
line decor

 Volume 8, Issue 4 (April 2021), Pages: 117-129

----------------------------------------------

 Original Research Paper

 Title: COVID-19 crisis and the continuous use of virtual classes

 Author(s): Yasser Ibrahim 1, 2, *, Imdadullah Hidayat-ur-Rehman 2

 Affiliation(s):

 1Sociocomputing Department, Faculty of Economics and Political Science, Cairo University, Giza, Egypt
 2Department of Management Information Systems, College of Business Administration, King Saud University, Riyadh, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4713-5095

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.04.014

 Abstract:

COVID-19 is a serious epidemic that has an unmistakable impact on all aspects of our lives, including the educational process. Most of the world has adopted Virtual Classes (VCs) to sustain teaching and learning. While prior research about such e-learning technologies has been focusing on the initial adoption, this research investigates the factors influencing the students’ desire and intention to continue using VCs, especially after the crisis subsides. This study extends the literature by developing a model that integrates pre-and post-adoption constructs and incorporates technological characteristics, namely, task technology fit, convenience, and compatibility into the Expectation Confirmation Model (ECM) to study the post-adoption Continuance Intention (CI) of VCs. The model is empirically validated using the partial least squares–structural equation modelling method and proved to have a reasonable description power (R2=62%) in terms of students’ CI. The survey empirical data is also supported by interviews with some students. The results support all the hypothesized relationships and confirm that the integration of technical characteristics in the ECM provides an appropriate framework to explain students’ intention to continue using VCs, which forms a good base for practitioners to consider a wide range of technological features for preparing applications. Yet, the model still requires to be extended with other stakeholders, including teachers, and other constructs like personal, psychological, social, and environmental factors. 

 © 2021 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Virtual classes, E-learning, Student satisfaction, Continuance intention, Task-technology-fit, Expectation-confirmation model

 Article History: Received 10 October 2020, Received in revised form 23 December 2020, Accepted 4 January 2021

 Acknowledgment:

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University, represented by the Research Centre at the College of Business Administration, for funding this research.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

  Ibrahim Y and Hidayat-ur-Rehman I (2021). COVID-19 crisis and the continuous use of virtual classes. International Journal of Advanced and Applied Sciences, 8(4): 117-129

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 

----------------------------------------------

 References (65)

  1. Akhter F and Ibrahim Y (2016). Intelligent accreditation system: A survey of the issues, challenges, and solution. International Journal of Advanced Computer Science and Applications, The Science and Information Organization, 7(1): 477-484. https://doi.org/10.14569/IJACSA.2016.070165   [Google Scholar]
  2. Al-Fraihat D, Joy M, and 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   [Google Scholar]
  3. Almarashdeh I (2016). Sharing instructors experience of learning management system: A technology perspective of user satisfaction in distance learning course. Computers in Human Behavior, 63: 249-255. https://doi.org/10.1016/j.chb.2016.05.013   [Google Scholar]
  4. Arbaugh JB (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24(1): 32-54. https://doi.org/10.1177/105256290002400104   [Google Scholar]
  5. Arnold R (1999). Will distance disappear in distance studies? Preliminary considerations on the didactic relevance of proximity and distance. Journal of Distance Education, 14(2): 1-9.   [Google Scholar]
  6. Arshad M and Akram MS (2018). Social media adoption by the academic community: Theoretical insights and empirical evidence from developing countries. International Review of Research in Open and Distributed Learning, 19: 3. https://doi.org/10.19173/irrodl.v19i3.3500   [Google Scholar]
  7. Bhattacherjee A (2001). Understanding information systems continuance: An expectation-confirmation model. Management Information Systems Quarterly, 25(3): 351–370. https://doi.org/10.2307/3250921   [Google Scholar]
  8. Bloomberg (2020). Markets: Five things you need to know to start your day. Bloomberg, New York, USA.   [Google Scholar]
  9. Bower BL and Hardy KP (2004). From correspondence to cyberspace: Changes and challenges in distance education. New Directions for Community Colleges, (128): 5-12. https://doi.org/10.1002/cc.169   [Google Scholar]
  10. Brown LG (1990). Convenience in services marketing. Journal of Services Marketing, 4(1): 53–59. https://doi.org/10.1108/EUM0000000002505   [Google Scholar]
  11. Carr WG and Mallam ML (1943). Chapter II: Effects of the world war on American education. Review of Educational Research, 13(1): 13-20. https://doi.org/10.3102/00346543013001013   [Google Scholar]
  12. Cheng YM (2014). What drives nurses' blended e-learning continuance intention? Educational Technology and Society, 17(4): 203-215.   [Google Scholar]
  13. Cheng YM (2019). How does task-technology fit influence cloud-based e-learning continuance and impact? Education and Training, 61(4): 480–499. https://doi.org/10.1108/ET-09-2018-0203   [Google Scholar]
  14. Davis FD, Bagozzi RP, and Warshaw PR (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   [Google Scholar]
  15. Delone WH and McLean ER (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4): 9-30. https://doi.org/10.1080/07421222.2003.11045748   [Google Scholar]
  16. Eshaghi Z and TaeiZadeh A (2015). A Conceptual framework in B2C e-commerce: Customer expectations and satisfaction relation with online purchasing behavior. In the International Conference on E-Business, E-Commerce, E-Management, E-Learning and E-Governance, London, UK: 93-98.   [Google Scholar]
  17. Fornell C and Larcker DF (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50. https://doi.org/10.1177/002224378101800104   [Google Scholar]
  18. Gill P, Stewart K, Treasure E, and Chadwick B (2008). Methods of data collection in qualitative research: Interviews and focus groups. British Dental Journal, 204(6): 291-295. https://doi.org/10.1038/bdj.2008.192   [Google Scholar] PMid:18356873
  19. Goodhue DL (1995). Understanding user evaluations of information systems. Management Science, 41(12): 1827-1844. https://doi.org/10.1287/mnsc.41.12.1827   [Google Scholar]
  20. Goodhue DL and Thompson RL (1995). Task-technology fit and individual performance. Management Information Systems Quarterly, 19(2): 213–236. https://doi.org/10.2307/249689   [Google Scholar]
  21. Guri-Rosenblit S (2005). Distance education” and “e-learning”: Not the same thing’, higher education. Kluwer Academic Publishers, 49(4): 467-493. https://doi.org/10.1007/s10734-004-0040-0   [Google Scholar]
  22. Hair JF, Anderson RE, Babin BJ, and Black WC (2010). Multivariate data analysis: A global perspective. Volume 7, Pearson, Upper Saddle River, USA.   [Google Scholar]
  23. Hair JF, Hult GTM, Ringle C, and Sarstedt M (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, Thousand Oaks, USA.   [Google Scholar]
  24. Henritius E, Löfström E, and Hannula MS (2019). University students’ emotions in virtual learning: A review of empirical research in the 21st century. British Journal of Educational Technology, 50(1): 80-100. https://doi.org/10.1111/bjet.12699   [Google Scholar]
  25. Henseler J, Ringle CM, and Sarstedt M (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1): 115-135. https://doi.org/10.1007/s11747-014-0403-8   [Google Scholar]
  26. Hiltz SR (1994). The virtual classroom: Learning without limits via computer networks. Intellect Books, Bristol, UK.   [Google Scholar]
  27. Houston K (2016). The book: A cover-to-cover exploration of the most powerful object of our time. WW Norton and Company, New York, USA.   [Google Scholar]
  28. Huang LC, Shiau WL, and Lin YH (2017). What factors satisfy e-book store customers? Development of a model to evaluate e-book user behavior and satisfaction. Internet Research, 27(3): 563-585. https://doi.org/10.1108/IntR-05-2016-0142   [Google Scholar]
  29. Hubackova S (2015). History and perspectives of e-learning. Procedia-Social and Behavioral Sciences, 191: 1187-1190. https://doi.org/10.1016/j.sbspro.2015.04.594   [Google Scholar]
  30. IMF (2020). World economic outlook, April 2020: The great lockdown. International Monetary Fund, Washington, USA.   [Google Scholar]
  31. Isaac O, Aldholay A, Abdullah Z, and Ramayah T (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers and Education, 136: 113-129. https://doi.org/10.1016/j.compedu.2019.02.012   [Google Scholar]
  32. Islam AN (2016). E-learning system use and its outcomes: Moderating role of perceived compatibility. Telematics and Informatics, 33(1): 48-55. https://doi.org/10.1016/j.tele.2015.06.010   [Google Scholar]
  33. Jin CH (2014). Adoption of e-book among college students: The perspective of an integrated TAM. Computers in Human Behavior, 41: 471-477. https://doi.org/10.1016/j.chb.2014.09.056   [Google Scholar]
  34. Jones L, Brown D, and Palumbo D (2020). Coronavirus: A visual guide to the economic impact. BBC News, London, UK.   [Google Scholar]
  35. Joo YJ, Park S, and Shin EK (2017). Students' expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69: 83-90. https://doi.org/10.1016/j.chb.2016.12.025   [Google Scholar]
  36. Karahanna E, Straub DW, and Chervany NL (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. Management Information Systems Quarterly: Management Information Systems, 23(2): 183-213. https://doi.org/10.2307/249751   [Google Scholar]
  37. Kock N and Lynn G (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7): 546-580. https://doi.org/10.17705/1jais.00302   [Google Scholar]
  38. Lai JY and Ulhas KR (2012). Understanding acceptance of dedicated e‐textbook applications for learning. The Electronic Library, 30(3): 321-338. https://doi.org/10.1108/02640471211241618   [Google Scholar]
  39. Liaw SS, Huang HM, and Chen GD (2007). Surveying instructor and learner attitudes toward e-learning. Computers and Education, 49(4): 1066-1080. https://doi.org/10.1016/j.compedu.2006.01.001   [Google Scholar]
  40. Lungu GF (1993). Educational policy-making in colonial Zambia: The case of higher education for Africans from 1924 to 1964. The Journal of Negro History, 78(4): 207-232. https://doi.org/10.2307/2717416   [Google Scholar]
  41. Madden AD, Bryson J, and Palimi J (2006). Information behavior in pre-literate societies. In: Spink A and Cole C (Eds.), New directions in human information behavior: 33-53. Springer, Dordrecht, Netherlands. https://doi.org/10.1007/1-4020-3670-1_3   [Google Scholar]
  42. Mokhtar SA, Katan H, and Hidayat-ur-Rehman I (2018). Instructors’ behavioural intention to use learning management system: An integrated TAM perspective. Journal Technology, Education, Management, Informatics, 7(3): 513-525.   [Google Scholar]
  43. Molen EHJ (2001). Virtual university? Educational environments for the future. Portland Press, London, UK.   [Google Scholar]
  44. NBSOC (2020). The national economy sustained steady recovery in October. National Bureau of Statistics of China. Beijing, China.   [Google Scholar]
  45. Nehusi KS (2010). The system of education in Kemet (ancient Egypt: An overview). Centre for Black and African Arts and Civilization (CBAAC), Lagos, Nigeria.   [Google Scholar]
  46. Noesgaard SS and Ørngreen R (2015). The effectiveness of e-learning: An explorative and integrative review of the definitions, methodologies and factors that promote e-learning effectiveness. Electronic Journal of E-learning, 13(4): 278-290.   [Google Scholar]
  47. Oliver RL (1977). Effect of expectation and disconfirmation on post exposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 62(4): 480-486. https://doi.org/10.1037/0021-9010.62.4.480   [Google Scholar]
  48. Oliver RL (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4): 460-469. https://doi.org/10.1177/002224378001700405   [Google Scholar]
  49. Ornstein AC, Levine DU and Gutek GL (2011). Foundations of education. Wadsworth Cengage Learning Publisher, Belmont, USA.   [Google Scholar]
  50. Podsakoff PM, MacKenzie SB, Lee JY, and Podsakoff NP (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5): 879-903. https://doi.org/10.1037/0021-9010.88.5.879   [Google Scholar] PMid:14516251
  51. RAM (2019). Online education market study 2019. Research and Markets, Dublin, Ireland.   [Google Scholar]
  52. Ratha DK, De S, Kim EJ, Plaza S, Seshan GK, and Yameogo ND (2020). COVID-19 crisis through a migration lens (English). Migration and Development Brief. Number 32, World Bank Group, Washington, USA.   [Google Scholar]
  53. Rehman HI, Akram MS, Malik A, Mokhtar SA, Bhatti ZA, and Khan MA (2020). Exploring the determinants of digital content adoption by academics: The moderating role of environmental concerns and price value. SAGE Open, 10(2): 2158244020931856. https://doi.org/10.1177/2158244020931856   [Google Scholar]
  54. Rogers E (1995). Diffusion of innovations. 4th Edition, Free Press, New York, USA.   [Google Scholar]
  55. Sangrà A, Vlachopoulos D, and Cabrera N (2012). Building an inclusive definition of e-learning: An approach to the conceptual framework. International Review of Research in Open and Distributed Learning, 13(2): 145-159. https://doi.org/10.19173/irrodl.v13i2.1161   [Google Scholar]
  56. Schwab K (2019). The global competitiveness report 2019: Insight report. World Economic Forum, Cologny, Switzerland.   [Google Scholar]
  57. SPA (2020). Distance education will be a strategic choice for the future. Saudi Press Agency, Riyadh, Saudi Arabia.   [Google Scholar]
  58. Stone RW and Baker-Eveleth L (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3): 984-990. https://doi.org/10.1016/j.chb.2012.12.007   [Google Scholar]
  59. Tan M and Shao P (2015). An ECM-ISC based study on learners' continuance intention toward e-learning. International Journal of Emerging Technologies in Learning, 10(4): 22-27. https://doi.org/10.3991/ijet.v10i4.4543   [Google Scholar]
  60. Venkatesh V, Morris MG, Davis GB, and Davis FD (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly: Management Information Systems Research Center, 27: 425-478. https://doi.org/10.2307/30036540   [Google Scholar]
  61. WBG (2020a). Remote learning, EdTech and COVID-19. World Bank Group, Washington, USA.   [Google Scholar]
  62. WBG (2020b). Social protection and COVID-19 (Coronavirus). World Bank Group, Washington, USA.   [Google Scholar]
  63. WBG (2020c). World Bank predicts sharpest decline of remittances in recent history. World Bank Group, Washington, USA.   [Google Scholar]
  64. WBG (2020d). World bank education and COVID-19. World Bank Group, Washington, USA.   [Google Scholar]
  65. Zhou T, Lu Y, and Wang B (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4): 760-767. https://doi.org/10.1016/j.chb.2010.01.013   [Google Scholar]