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Cited by 20Education during COVID-19 pandemicOnline and Blended LearningEducational Innovations and Technology

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Students’ acceptance of online learning in developing nations: scale development and validation

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Rajeb, Mehdi, Wang, Yurou, Man, Kaiwen, Morett, Laura M.·2023·Educational technology research and development
DOI 10.1007/s11423-022-10165-1PMC PMC9649008
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Abstract

Most education systems were severely impacted by the COVID-19 pandemic, and as a result, learning shifted from face-to-face to online in higher education institutions. This unprecedented shift in the learning environment caused substantial challenges for students. The situation was more severe in developing nations such as Bangladesh, which lacked available resources and knowledge of online education to support their students. Recent studies suggest that students resisted online learning in various developing nations. To support online learning in developing nations, this study develops the Acceptance of Online Learning (AOL) scale comprised of both institutional and student-related factors. To validate the AOL scale, the study collected data from 441 students across 30 higher education institutions in Bangladesh to determine the factors explaining students' acceptance of online learning using AOL measurements. The results showed that institutional factors, such as technological sufficiency, instructor efficiency, and technical assistance play significant roles in students' acceptance of online learning in developing nations. These findings will help education policymakers and administrators in developing nations to assess the needs of students with respect to online learning, and the AOL scale will assist in the evaluation of students' acceptance of online learning in these nations.

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  1. Abbasi, M. S. (2011). Culture, demography and individuals' technology acceptance behaviour: A PLS based structural evaluation of an extended model of technology acceptance in South-Asian country context. Unpublished doctoral dissertation. Brunel Business School.
  2. Adnan, M., & Anwar, K. (2020). Online learning amid the COVID-19 pandemic: Students' perspectives. Journal of Pedagogical Sociology and Psychology, 2(1), 45–51. https://doi.org/10.33902/JPSP.2020261309 · DOI
  3. Agormedah, E. K., Henaku, E. A., Ayite, D. M. K., & Ansah, E. A. (2020). Online learning in higher education during COVID-19 pandemic: A case of Ghana. Journal of Educational Technology and Online Learning, 3(3), 183–210. https://doi.org/10.31681/jetol.726441 · DOI
  4. 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. · DOI
  5. Alenezi, A. R., Karim, A. A., & Veloo, A. (2011). Institutional support and e-learning acceptance: An extension of the technology acceptance model. International Journal of Instructional Technology and Distance Learning, 8(2), 3–16.
  6. Alibudbud, R. (2021). On online learning and mental health during the COVID-19 pandemic: Perspectives from the Philippines. Asian Journal of Psychiatry. https://doi.org/10.1016/j.ajp.2021.102867 · DOI
  7. Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206 · DOI
  8. Alshare, K. A., Freeze, R. D., Lane, P. L., & Wen, H. J. (2011). The impacts of system and human factors on online learning systems use and learner satisfaction. Decision Sciences Journal of Innovative Education, 9(3), 437–461. https://doi.org/10.1111/j.1540-4609.2011.00321.x · DOI
  9. Ambarwati, R., Harja, Y. D., & Thamrin, S. (2020). The role of facilitating conditions and user habits: A case of Indonesian online learning platform. The Journal of Asian Finance, Economics and Business (JAFEB), 7(10), 481–489. https://doi.org/10.13106/jafeb.2020.vol7.no10.481 · DOI
  10. Andaleeb, S. S., Rahman, A., Rajeb, M., Akter, N., & Gulshan, S. (2012). Credibility of TV news in a developing country: The case of Bangladesh. Journalism and Mass Communication Quarterly, 89(1), 73–90. https://doi.org/10.1177/1077699011430060 · DOI
  11. Arghode, V., Brieger, E., & Wang, J. (2018). Engaging instructional design and instructor role in online learning environment. European Journal of Training and Development, 42, 366–380. https://doi.org/10.1108/EJTD-12-2017-0110 · DOI
  12. Asabere, N. Y. (2013). Benefits and challenges of mobile learning implementation: Story of developing nations. International Journal of Computer Applications, 73(1), 1. · DOI
  13. Ayodele, S., Endozo, A., & Ogbari, M. E. (2018). A study on factors hindering online learning acceptance in developing countries. In Proceedings of the 10th international conference on education technology and computers. Tokyo, Japan. · DOI
  14. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. · DOI · Paper page
  15. Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–computer Interaction, 34(9), 882–893. https://doi.org/10.1080/10447318.2017.1403220 · DOI
  16. Bhagat, K. K., Wu, L. Y., & Chang, C.-Y. (2016). Development and validation of the perception of students towards online learning (POSTOL). Journal of Educational Technology and Society, 19(1), 350–359.
  17. Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers and Education, 58(2), 843–855. https://doi.org/10.1016/j.compedu.2011.10.010 · DOI
  18. Blocher, J. M., De Montes, L. S., Willis, E. M., & Tucker, G. (2002). Online learning: Examining the successful student profile. Journal of Interactive Online Learning, 1(2), 1–12.
  19. Casaló, L. V., Flavián, C., & Guinalíu, M. (2008). The role of satisfaction and website usability in developing customer loyalty and positive word-of-mouth in the e-banking services. International Journal of Bank Marketing, 26(6), 399–417. https://doi.org/10.1108/02652320810902433 · DOI · Paper page
  20. Chang, C.-C., Yan, C.-F., & Tseng, J.-S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.818 · DOI
  21. Chen, K.-C., & Jang, S.-J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741–752. https://doi.org/10.1016/j.chb.2010.01.011 · DOI · Paper page
  22. Chen, Y.-J., & Chen, P.-C. (2007). Effects of online interaction on adult students’ satisfaction and learning. The Journal of Human Resource and Adult Learning, 3(2), 78–89.
  23. Chowdhury, R., & Sarkar, M. (2018). Education in Bangladesh: Changing contexts and emerging realities. In R. Chowdhury, M. Sarkar, F. Mojumder, & M. M. Roshid (Eds.), Engaging in educational research: Revisiting policy and practice in Bangladesh (pp. 1–18). Singapore: Springer. https://doi.org/10.1007/978-981-13-0708-9_1 · DOI
  24. Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018 · DOI
  25. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage Publications.
  26. Frøkjær, E., Hertzum, M., & Hornbæk, K. (2000). Measuring usability: Are effectiveness, efficiency, and satisfaction really correlated? Proceedings of the SIGCHI conference on human factors in computing systems (pp. 345–352). The Hague, The Netherlands. · DOI
  27. Gatian, A. W. (1994). Is user satisfaction a valid measure of system effectiveness? Information and Management, 26(3), 119–131. https://doi.org/10.1016/0378-7206(94)90036-1 · DOI
  28. George, G., & Camarata, M. (1996). Managing instructor cyberanxiety: The role of self-efficacy in decreasing resistance to change. Educational Technology, 36(4), 49–54.
  29. Glassman, M., Kuznetcova, I., Peri, J., & Kim, Y. (2021). Cohesion, collaboration and the struggle of creating online learning communities: Development and validation of an online collective efficacy scale. Computers and Education Open, 2, 100031. · DOI
  30. Goh, P. S. C., & Blake, D. (2021). E-readiness measurement tool: Scale development and validation in a Malaysian higher educational context. Cogent Education, 8(1), 1883829. https://doi.org/10.1080/2331186X.2021.1883829 · DOI
  31. Gómez-Carracedo, M., Andrade, J., López-Mahía, P., Muniategui, S., & Prada, D. (2014). A practical comparison of single and multiple imputation methods to handle complex missing data in air quality datasets. Chemometrics and Intelligent Laboratory Systems, 134, 23–33. https://doi.org/10.1016/j.chemolab.2014.02.007 · DOI
  32. Green, L. S., & Denton, B. (2012). Examination of factors impacting student satisfaction with a new learning management system. Turkish Online Journal of Distance Education, 13(3), 189–197.
  33. Greenland, S. (2011). Using log data to investigate the impact of (a) synchronous learning tools on LMS interaction. In Proceedings of the Australasian society for computers in learning in tertiary education (ASCILITE) conference. · DOI
  34. Gulati, S. (2008). Technology-enhanced learning in developing nations: A review. The International Review of Research in Open and Distributed Learning, 9(1), 1–16. · DOI
  35. Hasan, Z., & Islam, K. A. (2020). Academic, financial and administrative issues of online teaching during corona pandemic: The scenario of private universities in Bangladesh. International Journal of Accounting and Finance Review, 5(1), 116–122. https://doi.org/10.46281/ijafr.v5i1.630 · DOI
  36. Haththotuwa, P., & Rupasinghe, R. (2021). Adapting to online learning in higher education system during the covid-19 pandemic: A case study of Universities in Sri Lanka. Sri Lanka Journal of Social Sciences and Humanities, 1(2), 147–152. https://doi.org/10.4038/sljssh.v1i2.46 · DOI
  37. Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367. https://doi.org/10.1080/15391523.2011.10782576 · DOI · Paper page
  38. Hong, A. J., & Kim, H. J. (2018). College students’ digital readiness for academic engagement (DRAE) scale: Scale development and validation. The Asia-Pacific Education Researcher, 27(4), 303–312. https://doi.org/10.1007/s40299-018-0387-0 · DOI · Paper page
  39. Howshigan, S., & Nadesan, T. (2021). Students’ perceptions of online learning and teaching during COVID-19 pandemic: An empirical study in selected university in Sri Lanka. Asian Journal of Economics Business and Accounting, 21(8), 50–57. · DOI
  40. Jaffer, S., Ng’ambi, D., & Czerniewicz, L. (2007). The role of ICTs in higher education in South Africa: One strategy for addressing teaching and learning challenges. International Journal of Education and Development Using ICT, 3(4), 131–142.
  41. Jena, P. K. (2020). Impact of Covid-19 on higher education in India. International Journal of Advanced Education and Research (IJAER), 5(3), 77–81.
  42. Kabir, H., Nasrullah, S. M., Hasan, M. K., Ahmed, S., Hawlader, M. D. H., & Mitra, D. K. (2021). Perceived e-learning stress as an independent predictor of e-learning readiness: Results from a nationwide survey in Bangladesh. PLoS ONE, 16(10), e0259281. · DOI
  43. Kumar, A. A. (2021). Research note on online learning in India—Issues and challenges. Prabandhan: Indian Journal of Management, 14(8), 40–46. · DOI
  44. Larmuseau, C., Desmet, P., & Depaepe, F. (2019). Perceptions of instructional quality: Impact on acceptance and use of an online learning environment. Interactive Learning Environments, 27(7), 953–964. https://doi.org/10.1080/10494820.2018.1509874 · DOI
  45. Lee, J.-W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The Internet and Higher Education, 13(4), 277–283. · DOI
  46. Lee, Y.-C. (2008). The role of perceived resources in online learning adoption. Computers and Education, 50(4), 1423–1438. https://doi.org/10.1016/j.compedu.2007.01.001 · DOI
  47. Little, R. J. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202. · DOI · Paper page
  48. Lopez, R., Ahmad, J., & Zarim, N. Z. A. (2021). The psychometric properties of authentic leadership measurement in school context: Confirmatory factor analysis approach. Turkish Journal of Physiotherapy and Rehabilitation, 32(3), 6975–6986.
  49. Lyons, A., Kass-Hanna, J., Zucchetti, A., & Cobo, C. (2019). Leaving no one behind: Measuring the multidimensionality of digital literacy in the age of AI and other transformative technologies. https://hdl.handle.net/20.500.12381/332
  50. Maniruzzaman, M., Rahman, M. J., Al-MehediHasan, M., Suri, H. S., Abedin, M. M., El-Baz, A., & Suri, J. S. (2018). Accurate diabetes risk stratification using machine learning: Role of missing value and outliers. Journal of Medical Systems, 42(5), 1–17. https://doi.org/10.1007/s10916-018-0940-7 · DOI · Paper page
  51. Muthuprasad, T., Aiswarya, S., Aditya, K., & Jha, G. K. (2021). Students’ perception and preference for online education in India during COVID-19 pandemic. Social Sciences and Humanities Open, 3(1), 100101. https://doi.org/10.1016/j.ssaho.2020.100101 · DOI
  52. Nichols, M. (2008). Institutional perspectives: The challenges of e-learning diffusion. British Journal of Educational Technology, 39(4), 598–609. https://doi.org/10.1111/j.1467-8535.2007.00761.x · DOI
  53. Nugroho, Y. S., Anifah, L., Sulistiyo, E., Cahyaningtias, S., & Firmansyah, R. (2021). Analysis of learning quality with internet-based distance learning during the COVID-19 pandemic. International Journal of Recent Educational Research, 2(1), 96–110. https://doi.org/10.46245/ijorer.v2i1.81 · DOI
  54. Nunnally, J. C., & Bernstein, I. (1994). Psychometric theory. New York: McGraw Hill.
  55. Ohliati, J., & Abbas, B. S. (2019). Measuring students satisfaction in using learning management system. International Journal of Emerging Technologies in Learning. https://doi.org/10.3991/ijet.v14.i04.9427 · DOI
  56. Ouyang, F., & Scharber, C. (2017). The influences of an experienced instructor’s discussion design and facilitation on an online learning community development: A social network analysis study. The Internet and Higher Education, 35, 34–47. https://doi.org/10.1016/j.iheduc.2017.07.002 · DOI
  57. Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and Youth Services Review, 119, 105535. https://doi.org/10.1016/j.childyouth.2020.105535 · DOI
  58. Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., & Sindhi, S. (2018). Online education: Worldwide status, challenges, trends, and implications (Vol. 21, pp. 233–241). London: Taylor & Francis.
  59. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: Implications for further research. Journal of Marketing, 58(1), 111–124. · DOI · Paper page
  60. Pete, J., & Soko, J. (2020). Preparedness for online learning in the context of Covid-19 in selected sub-Saharan African countries. Asian Journal of Distance Education, 15(2), 37–47.
  61. Phutela, N., & Dwivedi, S. (2020). A qualitative study of students’ perspective on e-learning adoption in India. Journal of Applied Research in Higher Education, 12(4), 545–559. https://doi.org/10.1108/JARHE-02-2019-0041 · DOI
  62. Priatna, T., Maylawati, D., Sugilar, H., & Ramdhani, M. (2020). Key success factors of e-learning implementation in higher education. International Journal of Emerging Technologies in Learning (iJET), 15(17), 101–114. https://doi.org/10.3991/ijet.v15i17.14293 · DOI
  63. Rameez, A., Fowsar, M., & Lumna, N. (2020). Impact of Covid-19 on higher education sectors in Sri Lanka: A study based on south eastern university of Sri Lanka. Journal of Educational and Social Research 10(6), 341–349. https://doi.org/10.36941/jesr-2020-0132 · DOI
  64. Reddy, P., Sharma, B., & Chaudhary, K. (2021). Digital literacy: A review in the South Pacific. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-021-09280-4 · DOI
  65. Rios, T., Elliott, M., & Mandernach, B. J. (2018). Efficient instructional strategies for maximizing online student satisfaction. Journal of Educators Online, 15(3), n3. https://doi.org/10.9743/jeo.2018.15.3.7 · DOI
  66. Saha, A., Dutta, A., & Sifat, R. I. (2021). The mental impact of digital divide due to COVID-19 pandemic induced emergency online learning at undergraduate level: Evidence from undergraduate students from Dhaka City. Journal of Affective Disorders, 294, 170–179. https://doi.org/10.1016/j.jad.2021.07.045 · DOI
  67. Sarker, M. F. H., Al Mahmud, R., Islam, M. S., & Islam, M. K. (2019). Use of e-learning at higher educational institutions in Bangladesh: Opportunities and challenges. Journal of Applied Research in Higher Education, 11(2), 210–223. https://doi.org/10.1108/JARHE-06-2018-0099 · DOI
  68. Shahriar, S. H. B., Arafat, S., Sultana, N., Akter, S., Khan, M. M. R., Nur, J. E. H., & Khan, S. I. (2021). The transformation of education during the corona pandemic: Exploring the perspective of the private university students in Bangladesh. Asian Association of Open Universities Journal, 16(2), 161–176. https://doi.org/10.1108/AAOUJ-02-2021-0025 · DOI
  69. Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10–17. https://doi.org/10.1016/j.iheduc.2013.04.001 · DOI
  70. Shrestha, S., Haque, S., Dawadi, S., & Giri, R. A. (2021). Preparations for and practices of online education during the Covid-19 pandemic: A study of Bangladesh and Nepal. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10659-0 · DOI
  71. Simamora, R. M. (2020). The Challenges of online learning during the COVID-19 pandemic: An essay analysis of performing arts education students. Studies in Learning and Teaching, 1(2), 86–103. https://doi.org/10.46627/silet.v1i2.38 · DOI
  72. Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191–205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022 · DOI
  73. Sivo, S. A., Ku, C.-H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.2806 · DOI
  74. Srinivasan, A. (1985). Alternative measures of system effectiveness: Associations and implications. MIS Quarterly, 9(3), 243–253. · DOI
  75. Subedi, S., Nayaju, S., Subedi, S., Shah, S. K., & Shah, J. M. (2020). Impact of e-learning during COVID-19 pandemic among nursing students and teachers of Nepal. International Journal of Science and Healthcare Research, 5(3), 68–76.
  76. Sultana, T., & Khan, R. H. (2019). Investigating university students’ satisfaction on online class: Bangladesh perspective. Bangladesh Education Journal, 18(2), 23–64.
  77. Sun, Y., & Rogers, R. (2021). Development and validation of the online learning self-efficacy scale (OLSS): A structural equation modeling approach. American Journal of Distance Education, 35(3), 184–199. https://doi.org/10.1080/08923647.2020.1831357 · DOI
  78. Swan, K. (2001). Virtual interaction: Design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance Education, 22(2), 306–331. https://doi.org/10.1080/0158791010220208 · DOI · Paper page
  79. Tai, J.H.-M., Bellingham, R., Lang, J., & Dawson, P. (2019). Student perspectives of engagement in learning in contemporary and digital contexts. Higher Education Research and Development, 38(5), 1075–1089. https://doi.org/10.1080/07294360.2019.1598338 · DOI
  80. Tang, C. M., & Chaw, L. Y. (2016). Digital literacy: A prerequisite for effective learning in a blended learning environment? Electronic Journal of E-Learning, 14(1), 54–65.
  81. Tarhini, A., Elyas, T., Akour, M. A., & Al-Salti, Z. (2016). Technology, demographic characteristics and e-learning acceptance: A conceptual model based on extended technology acceptance model. Higher Education Studies, 6(3), 72–89. https://doi.org/10.5539/hes.v6n3p72 · DOI
  82. Teo, T. (2010). Development and validation of the e-learning acceptance measure (ElAM). The Internet and Higher Education, 13(3), 148–152. https://doi.org/10.1016/j.iheduc.2010.02.001 · DOI
  83. Tsai, C.-L., Cho, M.-H., Marra, R., & Shen, D. (2020). The self-efficacy questionnaire for online learning (SeQoL). Distance Education, 41(4), 472–489. https://doi.org/10.1080/01587919.2020.1821604 · DOI
  84. Tzafilkou, K., Perifanou, M., & Economides, A. A. (2021). Negative emotions, cognitive load, acceptance, and self-perceived learning outcome in emergency remote education during COVID-19. Education and Information Technologies, 26, 1–25. https://doi.org/10.1007/s10639-021-10604-1 · DOI
  85. Ullah, A., Ashraf, M., Ashraf, S., & Ahmed, S. (2021). Challenges of online learning during the COVID-19 pandemic encountered by students in Pakistan. Journal of Pedagogical Sociology and Psychology, 3(1), 36–44. · DOI
  86. Valencia-Arias, A., Chalela-Naffah, S., & Bermúdez-Hernández, J. (2019). A proposed model of e-learning tools acceptance among university students in developing countries. Education and Information Technologies, 24(2), 1057–1071. · DOI
  87. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872 · DOI · Paper page
  88. Vivolo, J. (2016). Understanding and combating resistance to online learning. Science Progress, 99(4), 399–412. https://doi.org/10.3184/003685016X14773090197742 · DOI
  89. Zhang, J., Cai, Z., Zhao, Z., & Ji, K. (2017). Cell phone-based online biochemistry and molecular biology medical education curriculum. Medical Education Online, 22(1), 1374135. · DOI
  90. Zhang, S.-N., Li, Y.-Q., Liu, C.-H., & Ruan, W.-Q. (2019). Critical factors in the identification of word-of-mouth enhanced with travel apps: The moderating roles of Confucian culture and the switching cost view. Asia Pacific Journal of Tourism Research, 24(5), 422–442. https://doi.org/10.1080/10941665.2019.1572630 · DOI

Metadata

Authors
Rajeb, M., Wang, Y., Man, K., & Morett, L. M.
Affiliations
University of Alabama (US)
Corresponding author
Rajeb, Mehdi
ORCID
Venue
Educational technology research and development
Publisher
Springer Science and Business Media LLC
Published
2022-11-10
Volume
71
Issue
2
Pages
767-792
ISSN
  • 1042-1629 (ISSN-L)
  • 1556-6501
DOI
10.1007/s11423-022-10165-1
PMC
PMC9649008
OpenAlex
W4308908380
Cited by
20
References
6
Citations by year
2026: 4 · 2025: 10 · 2024: 5 · 2023: 1
FWCI
5.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Educational Technology Research and Development (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Education › Education during COVID-19 pandemic
  • Social Sciences › Education › Online and Blended Learning
  • Social Sciences › Education › Educational Innovations and Technology
Keywords
scale development, scale validation, university students, developing countries, COVID-19 pandemic, technical assistance, institutional factors, student acceptance, Bangladesh

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CoChat. (2026). Citation check: Students’ acceptance of online learning in developing nations: scale development and validation. https://app.cochat.ai/papers/10.1007/s11423-022-10165-1
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Rajeb, M., Wang, Y., Man, K., & Morett, L. M. (2023). Students’ acceptance of online learning in developing nations: scale development and validation. Educational technology research and development. https://doi.org/10.1007/s11423-022-10165-1