Effect of Perceived Fear, Quality, and Self-Determination on Learners’ Retention Intention on MOOCs

References

• Blanco AF, Echaluce MLS, Peñalvo FJG. From massive access to cooperation: lessons learned and proven results of a hybrid xMOOC/cMOOC pedagogical approach to MOOCs. Int J Educ Technol High Educ. 2016;2016(13):14.
   Google Scholar
• Lai -C-C, Shih T-P, Ko W-C, Tang H-J, Hsueh P-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int J Antimicrob Agents. 2020;55(3):105924. doi:10.1016/j.ijantimicag.2020.105924
   PubMed Web of Science ®Google Scholar
• Mbelwa JT, Kimaro HC, Mussa B. Acceptability and Use of Mobile Health Applications in Health Information Systems: A Case of eIDSR and DHIS2 Touch Mobile Applications in Tanzania. Springer International Publishing; 2019:579–592.
   Google Scholar
• Lohr S. Remember the MOOCs? After near-death, they’re booming. The New York Times; 2020:1. Available from: https://www.nytimes.com/2020/05/26/technology/moocs-online-learning.html. Accessed September 28, 2022.
   Google Scholar
• Murphy MPA. COVID-19 and emergency eLearning: consequences of the securitization of higher education for post-pandemic pedagogy. Contemp Security Policy. 2020;1–14. doi:10.1080/13523260.2020.1761749
   Web of Science ®Google Scholar
• Hossain MN, Talukder MS, Khayer A, Bao Y. Investigating the factors driving adult learners’ continuous intention to use M-learning application: a fuzzy-set analysis. J Res Innov Teach Learn. 2020;14:245–270.
   Google Scholar
• García-Peñalvo FJ, Fidalgo-Blanco Á, Sein-Echaluce ML. An adaptive hybrid MOOC model: disrupting the MOOC concept in higher education. Telemat Info. 2018;35(4):1018–1030. doi:10.1016/j.tele.2017.09.012
   Web of Science ®Google Scholar
• Hossain MN, Hossain MY, Bao Y, Kumar N, Hoque M. A proposed model to design MOOCs through the lens of addressing graduate skill gap. High Educ Skills Work Based Learn. 2022. doi:10.1108/HESWBL-04-2021-0070
   Web of Science ®Google Scholar
• McGreal R, Mackintosh W, Cox G, Olcott D. Bridging the gap: micro-credentials for development. Int Rev Res Open Distributed Learn. 2022;23(3):288–302.
   Web of Science ®Google Scholar
• Che X, Luo S, Wang C, Meinel C. An attempt at MOOC localization for Chinese-speaking users. Int J Info Educ Technol. 2016;6(2):90. doi:10.7763/IJIET.2016.V6.665
   Google Scholar
• Tao D, Fu P, Wang Y, Zhang T, Qu XJ. Key characteristics in designing massive open online courses (MOOCs) for user acceptance: an application of the extended technology acceptance model. Interact Learn Environ. 2022;30(5):882–895.
   Web of Science ®Google Scholar
• Zhou L, Xue S, Li R. Extending the Technology Acceptance Model to explore students’ intention to use an online education platform at a University in China. SAGE Open. 2022;12(1):21582440221085259. doi:10.1177/21582440221085259
   Web of Science ®Google Scholar
• Yang H, Kim J, Kelly S, Merrill KJ. Learning in the online classroom: exploring the unique influence of social presence dimensions. Commun Stud. 2022;2022:1–18.
   Google Scholar
• Moore RL, Blackmon S. From the Learner’s perspective: a systematic review of MOOC learner experiences (2008–2021). Comput Educ. 2022;190:104596. doi:10.1016/j.compedu.2022.104596
   Web of Science ®Google Scholar
• Sallam MH, Martín-Monje E, Li YJ. Research trends in language MOOC studies: a systematic review of the published literature (2012–2018). Biomed Environ Sci. 2022;35(4):764–791. doi:10.3967/bes2022.100
   PubMed Web of Science ®Google Scholar
• DeLone WH, McLean ER, DeLone T. McLean model of information systems success: a ten-year update. J Manage Info Syst. 2003;19(4):9–30.
   Web of Science ®Google Scholar
• Wei X, Saab N, Admiraal WJ, Education H. Do learners share the same perceived learning outcomes in MOOCs? Identifying the role of motivation, perceived learning support, learning engagement, and self-regulated learning strategies. Internet High Educ. 2022;56:100880.
   Web of Science ®Google Scholar
• Lin T-J, Lin T-C, Potvin P, Tsai CC. Research trends in science education from 2013 to 2017: a systematic content analysis of publications in selected journals. Int J Sci Educ. 2019;41(3):367–387.
   Web of Science ®Google Scholar
• Ruipérez-Valiente JA. A macro-scale MOOC analysis of the socioeconomic status of learners and their learning outcomes. In: Socioeconomic Inclusion During an Era of Online Education. IGI Global; 2022:1–22.
   Google Scholar
• Moe WW, Schweidel DA. Online product opinions: incidence, evaluation, and evolution. Market Sci. 2012;31(3):372–386. doi:10.1287/mksc.1110.0662
   Web of Science ®Google Scholar
• Calisir F, Altin Gumussoy C, Bayraktaroglu AE, Karaali D. Predicting the intention to use a web‐based learning system: perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors Ergonom Manuf Service Indust. 2014;24(5):515–531.
   Web of Science ®Google Scholar
• Saeed A, Kazmierski M, Khan A, McShane D, Gomez A, Aslam A. Congenital lung lesions: preoperative three-dimensional reconstructed CT scan as the definitive investigation and surgical management. Eur J Pediatr Surg. 2013;23(01):053–056. doi:10.1055/s-0033-1333890
   PubMed Web of Science ®Google Scholar
• Lin K-M. e-Learning continuance intention: moderating effects of user e-learning experience. Educ Info Technol. 2011;56(2):515–526.
   Google Scholar
• Tung F-C, Chang S-C, Chou C-M. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int J Med Inform. 2008;77(5):324–335. doi:10.1016/j.ijmedinf.2007.06.006
   PubMed Web of Science ®Google Scholar
• Lee J-K, Lee W-K. The relationship of e-Learner’s self-regulatory efficacy and perception of e-Learning environmental quality. Comput Human Behav. 2008;24(1):32–47. doi:10.1016/j.chb.2006.12.001
   Web of Science ®Google Scholar
• Lin K-MJ. e-Learning continuance intention: moderating effects of user e-learning experience. Comput Educ. 2011;56(2):515–526. doi:10.1016/j.compedu.2010.09.017
   Web of Science ®Google Scholar
• Sun Y, Guo Y, Zhao Y. Understanding the determinants of learner engagement in MOOCs: an adaptive structuration perspective. Comput Educ. 2020;157:103963. doi:10.1016/j.compedu.2020.103963
   Web of Science ®Google Scholar
• Rai L, Chunrao D. Influencing factors of success and failure in MOOC and general analysis of learner behavior. Int J Info Educ Technol. 2016;6(4):262. doi:10.7763/IJIET.2016.V6.697
   Google Scholar
• Chen K-C, Jang S-J. Motivation in online learning: testing a model of self-determination theory. Comput Human Behav. 2010;26(4):741–752. doi:10.1016/j.chb.2010.01.011
   Web of Science ®Google Scholar
• Deci EL, Koestner R, Ryan RM. A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol Bull. 1999;125(6):627. doi:10.1037/0033-2909.125.6.627
   PubMed Web of Science ®Google Scholar
• Cho YH, Lee SY, Jeong DW, et al. Analysis of questioning technique during classes in medical education. BMC Med Educ. 2012;12(1):1–7. doi:10.1186/1472-6920-12-39
   PubMed Web of Science ®Google Scholar
• Deci EL, Koestner R, Ryan RM. The undermining effect is a reality after all—Extrinsic rewards, task interest, and self-determination: reply to Eisenberger, Pierce, and Cameron (1999) and Lepper, Henderlong, and Gingras (1999). Psychol Bull. 1999;125:692–700. doi:10.1037/0033-2909.125.6.692
   Web of Science ®Google Scholar
• Zhou M. Chinese university students’ acceptance of MOOCs: a self-determination perspective. Comput Educ. 2016;92:194–203. doi:10.1016/j.compedu.2015.10.012
   Web of Science ®Google Scholar
• Ryan RM, Deci EL. Self‐regulation and the problem of human autonomy: does psychology need choice, self‐determination, and will? J Pers Soc Psychol. 2006;74(6):1557–1586.
   Google Scholar
• Vansteenkiste M, Zhou M, Lens W, Soenens B. Experiences of autonomy and control among Chinese learners: vitalizing or immobilizing? J Educ Psychol. 2005;97(3):468. doi:10.1037/0022-0663.97.3.468
   Web of Science ®Google Scholar
• Giannakos MN, Pappas IO, Jaccheri L, Sampson DG. Understanding student retention in computer science education: the role of environment, gains, barriers and usefulness. journal article. Educ Info Technol. 2017;22(5):2365–2382. doi:10.1007/s10639-016-9538-1
   Web of Science ®Google Scholar
• Oh S, Ji H, Kim J, Park E, Del Pobil AP. Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Info Technol Tour. 2022;2022. doi:10.1007/s40558-022-00222-z
   Web of Science ®Google Scholar
• Oh S, Ji H, Kim J, Park E, Del Pobil AP. Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Info Technol Tour. 2022;24(1):109–126.
   Web of Science ®Google Scholar
• Napoli AR, Wortman PM. Psychosocial factors related to retention and early departure of two-year community college students. Res High Educ. 1998;39(4):419–455. doi:10.1023/A:1018789320129
   Web of Science ®Google Scholar
• Preece J, Maloney-Krichmar D, Abras CJ. History of online communities. Encyclopedia Commun. 2003;3(1023–1027):86.
   Google Scholar
• Turkle SJW, Nicolson L. Life on the screen: idein the age of the internet. 1995.
   Google Scholar
• Wellman B, Salaff J, Dimitrova D, Garton L, Gulia M, Haythornthwaite C. Computer networks as social networks: collaborative work, telework, and virtual community. Annu Rev Sociol. 1996;22(1):213–238. doi:10.1146/annurev.soc.22.1.213
   Web of Science ®Google Scholar
• Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci. 2000;46(2):186–204. doi:10.1287/mnsc.46.2.186.11926
   Web of Science ®Google Scholar
• Talukder MS, Chiong R, Bao Y, Malik BH, Systems D. Acceptance and use predictors of fitness wearable technology and intention to recommend. Industr Manag Data Syst. 2019;119:170–188. doi:10.1108/IMDS-01-2018-0009
   Web of Science ®Google Scholar
• Yıldırım M, Geçer E, Akgül Ö. The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychol Health Med. 2021;26(1):35–43. doi:10.1080/13548506.2020.1776891
   PubMed Web of Science ®Google Scholar
• Bish A, Yardley L, Nicoll A, Michie S. Factors associated with uptake of vaccination against pandemic influenza: a systematic review. Vaccine. 2011;29(38):6472–6484. doi:10.1016/j.vaccine.2011.06.107
   PubMed Web of Science ®Google Scholar
• Hossain MN, Hossain MY, Bao Y, Kumar N, Hoque MR. A proposed model to design MOOCs through the lens of addressing graduate skill gap. High Educ Skills Work Based Learn. 2022. doi:10.1108/HESWBL-04-2021-0070
   Web of Science ®Google Scholar
• Khayer A, Talukder SH, Bao Y, Hossain MN. Application-based mobile payment systems: continuance intention and intention to recommend. 2021:1.
   Google Scholar
• Kumar N, Hossain MY, Jin Y, Safeer AA, Chen T. Impact of performance lower than expectations on work behaviors: the moderating effect of status mutability and mediating role of regulatory focus. Psychol Res Behav Manag. 2021;14:2257–2270. doi:10.2147/prbm.S342562
   PubMed Web of Science ®Google Scholar
• Liao Q, Wu P, Wing Tak Lam W, Cowling BJ, Fielding R. Trajectories of public psycho-behavioural responses relating to influenza A (H7N9) over the winter of 2014–15 in Hong Kong. Psychol Health. 2019;34(2):162–180. doi:10.1080/08870446.2018.1515436
   PubMed Web of Science ®Google Scholar
• Shah D. By the numbers: MOOCs in 2020. The Reportl; 2020. Available from: https://www.classcentral.com/report/mooc-stats-2020/. Accessed September 28, 2022.
   Google Scholar
• David S, Shah A. MOOCs, graduate skills gaps, and employability: a qualitative systematic review of the literature. IRRODL. 2016;17(5):67–90.
   Google Scholar
• Yang M, Shao Z, Liu Q, Liu C. Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educ Technol Res Dev. 2017;65(5):1195–1214. doi:10.1007/s11423-017-9513-6
   Web of Science ®Google Scholar
• Chiu CM, Chiu CS, Chang HC. Examining the integrated influence of fairness and quality on learners’ satisfaction and Web‐based learning continuance intention. Info Syst J. 2007;17(3):271–287. doi:10.1111/j.1365-2575.2007.00238.x
   Web of Science ®Google Scholar
• Lewis W, Agarwal S, Sambamurthy V. Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Quart. 2003;27:657–678. doi:10.2307/30036552
   Web of Science ®Google Scholar
• Li B, Wang X, Tan SC. What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors. Comput Human Behav. 2018;85:385–395. doi:10.1016/j.chb.2018.04.028
   Web of Science ®Google Scholar
• Bhattacherjee A, Premkumar G. Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Quart. 2004;28:229–254. doi:10.2307/25148634
   Web of Science ®Google Scholar
• Zhang M, Yin S, Luo M. Learner control, user characteristics, platform difference, and their role in adoption intention for MOOC learning in China. Austral J Educ Technol. 2017;33(1):15.
   Web of Science ®Google Scholar
• Ryan RM, Connell JP. Perceived locus of causality and internalization: examining reasons for acting in two domains. J Pers Soc Psychol. 1989;57(5):749. doi:10.1037/0022-3514.57.5.749
   PubMed Web of Science ®Google Scholar
• Li X, Chen Y, Gong X. MOOCs in China: a review of literature, 2012–2016. In: New Ecology for education—communication X Learning. Springer; 2017:21–32.
   Google Scholar
• Law TY, Leung FC, Chiu DK, et al. Mobile learning usage of LIS students in Mainland China. IJSSOE. 2019;9(2):12–34.
   Google Scholar
• Gravetter FJ, Forzano L-AB. Research Methods for the Behavioral Sciences. Cengage Learning; 2018.
   Google Scholar
• Sharma SK, Al-Badi AH, Govindaluri SM, Al-Kharusi MH. Predicting motivators of cloud computing adoption: a developing country perspective. Comp Human Behav. 2016;62:61–69.
   Web of Science ®Google Scholar
• Ooi K-B, Hew -J-J, Lee V-H. Could the mobile and social perspectives of mobile social learning platforms motivate learners to learn continuously? Comput Educ. 2018;120:127–145. doi:10.1016/j.compedu.2018.01.017
   Web of Science ®Google Scholar
• Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879. doi:10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Harman HH. Modern Factor Analysis. University of Chicago Press; 1976.
   Google Scholar
• Kock N. Common method bias in PLS-SEM: a full collinearity assessment approach. Int J e-Collaboration. 2015;11(4):1–10.
   Web of Science ®Google Scholar
• Hair JF, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications; 2016.
   Google Scholar
• Hair JF, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications; 2013.
   Google Scholar
• Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Adv Int Market. 2009;20(1):277–319.
   Google Scholar
• Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Market Res. 1981;18:39–50. doi:10.1177/002224378101800104
   Web of Science ®Google Scholar
• Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Market Sci. 2014;43(1):115–135. doi:10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Ringle CM, Sarstedt M. Gain more insight from your PLS-SEM results: the importance-performance map analysis. Ind Manage Data Syst. 2016;116(9):1865–1886. doi:10.1108/IMDS-10-2015-0449
   Web of Science ®Google Scholar
• Aldholay AH, Isaac O, Abdullah Z, Ramayah T. The role of transformational leadership as a mediating variable in DeLone and McLean information system success model: the context of online learning usage in Yemen. Telemat Inform. 2018;35(5):1421–1437. doi:10.1016/j.tele.2018.03.012
   Web of Science ®Google Scholar
• Guthrie D. MOOCs are Toast or at Least Should Be. Forbes. 2013.
   Google Scholar
• Dost S, Hossain A, Shehab M, Abdelwahed A, Al-Nusair L. Perceptions of medical students towards online teaching during the COVID-19 pandemic: a national cross-sectional survey of 2721 UK medical students. BMJ open. 2020;10(11):e042378. doi:10.1136/bmjopen-2020-042378
   PubMed Web of Science ®Google Scholar
• Chang C-S, Chen S-Y, Lan Y-T. Service quality, trust, and patient satisfaction in interpersonal-based medical service encounters. BMC Health Serv Res. 2013;13(1):1–11. doi:10.1186/1472-6963-13-22
   PubMed Web of Science ®Google Scholar
• Halawi L, McCarthy R. Measuring faculty perceptions of blackboard using the technology acceptance model. Issues Info Syst. 2007;8(2):160.
   Google Scholar
• Leclercq T, Hammedi W, Poncin I. The boundaries of gamification for engaging customers: effects of losing a contest in online co-creation communities. J Interact Market. 2018;44:82–101. doi:10.1016/j.intmar.2018.04.004
   Web of Science ®Google Scholar
• Marble RP. A system implementation study: management commitment to project management. Info Manage Sci. 2003;41(1):111–123. doi:10.1016/S0378-7206(03)00031-4
   Web of Science ®Google Scholar
• Chiu C-H, Guu Y-K, Liu C-H, Pan T-M, Cheng W. Immune responses and gene expression in white shrimp, Litopenaeus vannamei, induced by Lactobacillus plantarum. Fish Shellfish Immunol. 2007;23(2):364–377. doi:10.1016/j.fsi.2006.11.010
   PubMed Web of Science ®Google Scholar
• Guàrdia L, Maina M, Sangrà AJ, Carazo B. MOOC design principles: a pedagogical approach from the learner’s perspective. Ginecologia y obstetricia de Mexico. 2013;81(8):472–476.
   PubMedGoogle Scholar