Students’ satisfaction and continued intention toward e-learning: a theory-based study

References

• Rossi PG. Learning environment with elements of artifi cial intelligence. J e-Learn Knowl Soc. 2009;5(1):191–8.
   Google Scholar
• Masic I. E-learning as new method of medical education. Acta Inform Med. 2008;16:102–117.
   PubMedGoogle Scholar
• Luwei L, Ruyu Z, Siyang Y, et al. Simulation training for ceramic crown preparation in the dental setting using a virtual educational system. Eur J Den Educ. 2020;24(2):199–205. .
   Web of Science ®Google Scholar
• Hilburg R, Patel N, AMbruso S, et al. Medical education during the coronavirus disease-2019 pandemic: learning from a distance’. Adv Chronic Kidney Dis. 2020;27(5):412–417.
   PubMed Web of Science ®Google Scholar
• Durfee S, Goldenson R, Gill R, et al. Medical student education roadblock due to COVID19: virtual radiology core clerkship to the rescue. Acad Radiol. 2020;27(10):1416–1466.
   Web of Science ®Google Scholar
• Wootton AR, McCuistian C, Legnitto Packard DA, et al. Overcoming technological challenges: lessons learned from a telehealth counseling study. Telemed J E Health. 2020;26(10):1278–1283.
   Web of Science ®Google Scholar
• Halphin PA, Lockwood MK. The use of Twitter and Zoom videoconferencing in healthcare professions seminar course benefits students at a commuter college. Adv Physiol Educ. 2019;43:264–294.
   Web of Science ®Google Scholar
• Vitoria L, Mislinawati M, Nurmasyitah N. Students’ perceptions on the implementation of e-learning: helpful or unhelpful? J Physics. 2018;1088:1–6.
   Google Scholar
• Popovici A, Mironov C. Students’ perception on using e-learning technologies. Procedia Soc Behav Sci. 2015;180:1514–1519.
   Google Scholar
• Asiry MA. Dental students’ perceptions of an online learning. Saudi Dent J. 2017;29(4):167–170.
   Web of Science ®Google Scholar
• Maheshwari S, Zheleva B, Rajasekhar V, et al. E-Teaching in pediatric cardiology: a paradigm shift. Ann Pediatr Cardiol. 2015;8(1):10–13.
   Web of Science ®Google Scholar
• Alkhowailed MS, Rasheed Z, Shariq A, et al. Digitalization plan in medical education during COVID-19 lockdown. Inform Med Unlocked. 2020;20:100432.
   PubMedGoogle Scholar
• Tabatabai S. COVID-19 impact and virtual medical education. J Adv Med Educ Prof. 2020;8(3):140–143.
   PubMedGoogle Scholar
• Bhattacherjee A. An empirical analysis of the antecedents of electronic commerce service continuance. Decis Support Syst. 2001;32(2):201–214.
   Web of Science ®Google Scholar
• Hsu MH, Chiu CM, Ju TL. Determinants of continued use of the WWW: an integration of two theoretical models. Ind Manage Data Syst. 2004;104(9):766–775.
   Web of Science ®Google Scholar
• McKinney V, Yoon K, Zahedi F. The measurement of web-customer satisfaction: an expectation and disconfirmation approach. Inf Syst Res. 2002;13(3):296–315.
   Web of Science ®Google Scholar
• Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989;13(3):319–340.
   Web of Science ®Google Scholar
• Spreng R, Chiou J. A cross-cultural assessment of the satisfaction formation process. Euro J Mark. 2002;36(7/8):829–839.
   Google Scholar
• Lee MC. Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation-confirmation model. Comput Educ. 2010;54(2):506–516.
   Web of Science ®Google Scholar
• Chou HK, Lin IC, Woung LC, et al. Engagement in e-learning opportunities: an empirical study on patient education using expectation confirmation theory. J Med Styt. 2012;36(3):1697–1706.
   Google Scholar
• Ajzen I. Attitudes, personality, and behavior. Dorsey Press: Chicago; 1988.
   Google Scholar
• Ajzen I. The theory of planned behavior. Organ Behav Hum Decis. 1991;50:179–211.
   Web of Science ®Google Scholar
• Cheon J, Lee S, Crooks S, et al. An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Comput Educ. 2012;59(3):1054–1064.
   Web of Science ®Google Scholar
• Azizi SM, Khatony A. Investigating factors affecting on medical sciences students’ intention to adopt mobile learning. BMC Med Educ. 2019;19(1):381.
   Web of Science ®Google Scholar
• Robinson RP, Doverspike D. Factors predicting the choice of an online versus a traditional course. Teach Psychol. 2006;33:64–68.
   Web of Science ®Google Scholar
• Hadadgar A, Changiz T, Masiello I, et al. Applicability of the theory of planned behavior in explaining the general practitioners eLearning use in continuing medical education. BMC Med Educ. 2016;16(1):215.
   PubMed Web of Science ®Google Scholar
• Gagnon MP, Legare F, Labrecque M, et al. Perceived barriers to completing an e-learning program on evidence-based medicine. Inform Prim Care. 2007;15(2):83–91.
   PubMedGoogle Scholar
• Hadadgar A, Changiz T, Dehghani Z, et al. A theory-based study of factors explaining general practitioners’ intention to use and participation in electronic continuing medical education. J Contin Educ Health Prof. 2016;36(4):290–294. .
   Web of Science ®Google Scholar
• Nkenke E, Vairaktaris E, Bauersachs A, et al. Acceptance of virtual dental implant planning software in an undergraduate curriculum: a pilot study. BMC Med Educ. 2012;12:90.
   Web of Science ®Google Scholar
• Davis FD, Venkatesh VT. Toward preprototype user acceptance testing of new information systems implications for software project management. IEEE Trans Eng Manage. 2004;51(1):31–64.
   Web of Science ®Google Scholar
• Hopkins L, Hampton BS, Abbott JF, et al. To the point: medical education, technology, and the millennial learner. Am J Obstet Gynecol. 2018;218(2):188–192. .
   PubMed Web of Science ®Google Scholar
• Berge ZL. Barriers to online teaching in post-secondary institutions: can policy changes f ix it? Online J Dist Learn Admin. 2005;1(2):1–13.
   Google Scholar
• Cheng YM. Investigating medical professionals’ continuance intention of the cloud-based e-learning system: an extension of expectation–confirmation model with flow theory. J Enterp Inf Manag. 2020; 34(4):1169-1202.
   Google Scholar
• Weng C, Tsai CC, Apollo W. Social support as a neglected e-learning motivator affecting trainee’s decisions of continuous intentions of usage. Australas J Educ Technol. 2015;31(2):166–192.
   Web of Science ®Google Scholar
• Ramírez-Correa PE, Arenas-Gaitán J, Rondán-Cataluña FJ. Gender and acceptance of e-learning: a multi-group analysis based on a structural equation model among collgege students in Chile and Spain. PLoS One. 2015;14(10):1–17.
   Google Scholar
• Cheng YM. Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention. Inform Tech People. 2014a;27(3):230–258.
   Web of Science ®Google Scholar
• Cheng YM. How does task-technology fit influence cloud-based e-learning continuance and impact? Educ Train. 2019;61:4.
   Web of Science ®Google Scholar
• Lin WS, Wang CH. Antecedences to continued intentions of adopting e-learning system in blended learning instruction: a contingency framework based on models of information system success and task-technology fit. Comput Educ. 2012;58(1):88–99.
   Web of Science ®Google Scholar
• Buabbas AJ, Al-Shawaf HM, Almajran AA. Health sciences students’ self-assessment of information and communication technology skills and attitude toward e-learning. JMIR Med Educ. 2016;2(1):e9.
   Google Scholar
• Ya-Ming S, Yu-Chiung H, Meng-Huei S, et al. Evaluation of a mobile learning system to support correct medication use for health promotion. Int J Manage Econ Soc Sci. 2019;8(3):242–252.
   Google Scholar
• Bolton RN, Lemon KN. A dynamic model of customers’ usage of services: usage as an antecedent and consequence of satisfaction. J Mark Res. 1999;36(5):171–186.
   Google Scholar
• Chen NH. Extending a TAM–TTF model with perceptions toward telematics adoption. Asia Pacific J Mark Logist. 2019;31(1):37–54.
   Web of Science ®Google Scholar
• Larsen TJ, Sørebø AM, Sørebø Ø. The role of task-technology fit as users’ motivation to continue information system use. Comput Hum Behav. 2009;25(3):778–784.
   Web of Science ®Google Scholar
• Huang TCK, Wu IL, Chou CC. Investigating use continuance of data mining tools. Int J Inf Manag. 2013;33(5):791–801.
   Web of Science ®Google Scholar
• Shiue Y. Investigating the sources of teachers’ instructional technology use through the decomposed theory of planned behavior. J Educ Comput Res. 2007;36(4):425–453.
   Google Scholar
• Chu TH, Chen YY. With good we become good: understanding e-learning adoption by theory of planned behavior and group influences. Comput Educ. 2016; 92(1): 37-52.
   Google Scholar
• Raza SA, Umer A, Qazi W, et al. The effects of attitudinal, normative, and control beliefs on M-learning adoption among the students of higher education in Pakistan. J Educ Comput Res. 2017;56(4):563–588.
   Google Scholar