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Many Higher Education Institutions in India have started to include Massive Open Online Course (MOOC) as a part of their curriculum. Yet little research has been done to understand the factors that affect the users’ sustained interest to recurrently enroll and complete MOOC courses throughout their lifetime. A sample of 316 students from Higher Education Institutes in India participated in this survey. Partial Least Square Structural Equation Modeling (PLS-SEM) using smartPLS was employed to assess the structural model. The structural model used is a combination of constructs from technology acceptance theories namely perceived usefulness, perceived ease of use, attitude, facilitating condition and continuous use intention of MOOCs, with perceived reputation and MOOCs self-efficacy as external variables. The results not only proved the applicability of basic TAM constructs in understanding the behavioral intention of MOOCs but also the significance of external variables, particularly the role of perceived reputation in influencing the perceived usefulness of MOOCs.


MOOC Perceived Reputation TAM Self-Efficacy Technology Acceptance Model Digital Education

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Rajam, V., Banerjee, S., & Alok, S. (2024). Can MOOC be a medium of lifelong learning? Examining the role of Perceived Reputation and Self-efficacy on Continuous Use Intention of MOOC. Journal of E-Learning and Knowledge Society, 20(1), 1-14.


  1. Afthanorhan, A., Ghazali, P. L., & Rashid, N. (2021, May). Discriminant validity: A comparison of CBSEM and consistent PLS using Fornell & Larcker and HTMT approaches. Journal of Physics: Conference Series, 1874(1), 012085. IOP Publishing.
  2. AL-Nuaimi, M.N., Al Sawafi, O.S., Malik, S.I., & Al-Maroof, R.S. (2022). Extending the unified theory of acceptance and use of technology to investigate determinants of acceptance and adoption of learning management systems in the post-pandemic era: a structural equation modeling approach. Interactive Learning Environments, 1–27.
  3. Alraimi, K.M., Zo, H., & Ciganek, A.P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38.
  4. Ansari, S.P., & Biswal, A. (2023). Unpacking the MOOC experience: insights from Indian Postgraduate Students in Education. Journal of e-Learning and Knowledge Society, 19(3), 59-64.
  5. Badali, M., Hatami, J., Banihashem, S. K., Rahimi, E., Noroozi, O., & Eslami, Z. (2022). The role of motivation in MOOCs’ retention rates: a systematic literature review. Research and Practice in Technology Enhanced Learning, 17(1), 1-20.
  6. Bandura, A. (1986). Prentice-Hall series in social learning theory. Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ.
  7. Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in massive open online courses: Examining aspects of language and social engagement. Computers & Education, 94, 49-60.
  8. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351e370.
  9. Blackwell, V. K., & Wiltrout, M. E. (2021). Learning During COVID-19: Engagement and Attainment in an Introductory Biology MOOC. EMOOCs 2021, 219-236.
  10. Bordoloi, R., Das, P., & Das, K. (2020). Lifelong learning opportunities through MOOCs in India. Asian Association of Open Universities Journal, 15(1), 83-95.
  11. Brauweiler, H., & Yerimpasheva, A. (2021). Moving to blended learning in the post-pandemic era. In J. Dyczkowska, The impact of COVID-19 on accounting, business practice and education (1st ed., pp. 104-120). Publishing House of Wroclaw University of Economics and Business.
  12. Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. Journal of Computing in Higher Education, 34(3), 844-867.
  13. Chauhan, S., Goyal, S., Bhardwaj, A. K., & Sergi, B. S. (2022). Examining continuance intention in business schools with digital classroom methods during COVID-19: a comparative study of India and Italy. Behaviour & Information Technology, 41(8), 1596-1619.
  14. Cheah, J. H., Memon, M. A., Chuah, F., Ting, H., & Ramayah, T. (2018). Assessing reflective models in marketing research: A comparison between pls and plsc estimates. International Journal of Business & Society, 19(1).
  15. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & education, 59(3), 1054-1064.
  16. Chiu, C. M., & Wang, E. T. G. (2008). Understanding web-based learning continuance intention: The role of subjective task value. Information & Management, 45,194-201.
  17. Cho, M. H., & Shen, D. (2013). Self-regulation in online learning. Distance education, 34(3), 290-301.
  18. Chris Impey & Martin Formanek. (2021). MOOCS and 100 Days of COVID: Enrollment surges in massive open online astronomy classes during the coronavirus pandemic. Social Sciences & Humanities Open, 4(1).
  19. Cioppi, M., Curina, I., Forlani, F., & Pencarelli, T. (2019). Online presence, visibility and reputation: a systematic literature review in management studies. Journal of Research in Interactive Marketing.
  20. Cisel, M. (2014). Analyzing completion rates in the first French xMOOC. Proceedings of the European MOOC Stakeholder Summit, 26, 51.
  21. Dang, A., Khanra, S., & Kagzi, M. (2022). Barriers towards the continued usage of massive open online courses: A case study in India. The International Journal of Management Education, 20(1), 100562.
  22. Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092.
  23. Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International journal of human-computer studies, 45(1), 19-45.
  24. 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.
  25. Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in human behavior, 49, 272-281.
  26. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten year update. Journal of Management Information Systems, 19(4), 9-30.
  27. Dhawal Shah (2020, Sep 07). MOOCWatch 25: Advent of Online Degrees in India.
  28. Dillahunt, T., Wang, Z., & Teasley, S. D. (2014). Democratizing higher education: Exploring MOOC use among those who cannot afford a formal education. International Review of Research in Open and Distributed Learning, 15(5), 177-196.
  29. El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763.
  30. Fombrun, C., & Van Riel, C. (1997). The reputational landscape. Corporate reputation review, 1-16.
  31. Goopio, J., & Cheung, C. (2021). The MOOC dropout phenomenon and retention strategies. Journal of Teaching in Travel & Tourism, 21(2), 177-197.
  32. Government of India. Ministry of Human Resource Development. (2020). National Education Policy 2020.
  33. Greene, J.A., Oswald, C.A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal, 52(5), 925-955.
  34. Gupta, K. P., & Maurya, H. (2020). Adoption, completion and continuance of MOOCs: A longitudinal study of students’ behavioural intentions. Behaviour & information technology, 41(3), 611-628.
  35. Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial management & data systems.
  36. Haryati, S., Sukarno, S., and Purwanto, S. (2021). Implementation of online education during the global Covid-19 pandemic: Prospects and challenges. Cakrawala Pendidikan, 40, 604–612. doi: 10.21831/cp.v40i3.42646
  37. Henseler, J., Ringle, C. M., & 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.
  38. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
  39. Huanhuan, W., & Xu, L. (2015, September). Research on technology adoption and promo- tion strategy of MOOC. In Software Engineering and Service Science (ICSESS), 2015 6th IEEE International Conference (pp. 907–910). IEEE.
  40. Huanhuan, W., & Xu, L. (2015, September). Research on technology adoption and promotion strategy of MOOC. In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 907-910). IEEE.
  41. Jacobsen, D. Y. (2019). Dropping out or dropping in? A connectivist approach to understanding participants’ strategies in an e-learning MOOC pilot. Technology, Knowledge and Learning, 24(1), 1-21.
  42. Jones, K., & Sharma, R. (2020). On reimagining a future for online learning in the post-COVID era. Kevin Jones & Ravi Sharma (2020). Reimagining A Future For Online Learning In The Post-COVID Era. First posted on
  43. Kaveri, A., Gunasekar, S., Gupta, D., & Pratap, M. (2015, October). Decoding the Indian MOOC learner. In 2015 IEEE 3RD International Conference on MOOCS, Innovation and technology in education (MITE) (pp. 182-187). IEEE.
  44. Khalil, H., & Ebner, M. (2014). MOOCs completion rates and possible methods to improve retention-A literature review. EdMedia+ innovate learning, 1305-1313.
  45. Khan, A. U., Khan, K. U., Atlas, F., Akhtar, S., & Farhan, K. H. A. N. (2021). Critical factors influencing moocs retention: The mediating role of information technology. Turkish Online Journal of Distance Education, 22(4), 82-101.
  46. Khan, I.U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., & Khan, S.U. (2018). Predicting the Acceptance of MOOCs in a Developing Country: Application of Task-Technology Fit Model, Social Motivation, and Self-Determination Theory. Telematics and Informatics, 35(4), 964-978.
  47. Kim, R., & Song, H.-D. (2021). Examining the Influence of Teaching Presence and Task-Technology Fit on Continuance Intention to Use MOOCs. The Asia-Pacific Education Researcher, 1-14. 10.1007/s40299-021-00581-x.
  48. Kizilcec, R.F., Piech, C., & Schneider, E. (2013, April). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the third international conference on learning analytics and knowledge (pp. 170-179).
  49. Kline, R.B. (2011). Principles and practice of structural equation modeling (3a ed.). New York, NY: Guilford.
  50. Kundu, A. (2020). Toward a framework for strengthening participants’ self-efficacy in online education. Asian Association of Open Universities Journal.
  51. Kuo, T.M., Tsai, C.C., & Wang, J.C. (2021). Linking web-based learning self-efficacy and learning engagement in MOOCs: The role of online academic hardiness. The Internet and Higher Education, 51, 100819.
  52. Lee, D., Watson, S.L., & Watson, W.R. (2020). The relationships between self-efficacy, task value, and self-regulated learning strategies in massive open online courses. International Review of Research in Open and Distributed Learning, 21(1), 23-39.
  53. 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.
  54. Luis V. Casalo, Carlos Flavián & Miguel Guinalíu (2007). The Influence of Satisfaction, Perceived Reputation and Trust on a Consumer's Commitment to a Website. Journal of Marketing Communications, 13(1), 1-17. 
  55. DOI: 10.1080/13527260600951633
  56. Meet, R. K., & Kala, D. (2021). Trends and Future Prospects in MOOC Researches: A Systematic Literature Review 2013-2020. Contemporary Educational Technology, 13(3).
  57. MHRD (2020). All India Survey on Higher Education 2019-20. Government of India. Department of Higher Education, New Delhi
  58. Milan, G.S., Eberle, L., & Bebber, S. (2015). Perceived value, reputation, trust, and switching costs as determinants of customer retention. Journal of Relationship Marketing, 14(2), 109-123.
  59. Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in human behavior, 45, 359-374.
  60. Munisamy, S., Jaafar, N. I. M., & Nagaraj, S. (2014). Does reputation matter? case study of undergraduate choice at a premier university. Asia-Pacific Education Researcher, 23(3), 451e462.
  61. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
  62. Punjani, K. K., & Mahadevan, K. (2022). Transitioning to online learning in higher education: Influence of Awareness of COVID-19 and Self-Efficacy on Perceived Net Benefits and Intention. Education and Information Technologies, 27(1), 291-320.
  63. Pursel, B. K., Zhang, L., Jablokow, K. W., Choi, G. W., & Velegol, D. (2016). Understanding MOOC students: motivations and behaviours indicative of MOOC completion. Journal of Computer Assisted Learning, 32(3), 202-217.
  64. Rajam, V., Reddy, A. B., & Banerjee, S. (2021). Explaining caste-based digital divide in India. Telematics and Informatics, 65, 101719.
  65. Ramayah, T., Yeap, J. A., Ahmad, N. H., Halim, H. A., & Rahman, S. A. (2017). Testing a confirmatory model of Facebook usage in SmartPLS using consistent PLS. International Journal of Business and Innovation, 3(2), 1-14.
  66. Rambe, P., & Moeti, M. (2017). Disrupting and democratizing higher education provision or entrenching academic elitism: towards a model of MOOCs adoption at African universities. Educational Technology Research and Development, 65(3), 631-651.
  67. Rasheed, R. A., Kamsin, A., Abdullah, N. A., Zakari, A., & Haruna, K. (2019). A systematic mapping study of the empirical MOOC literature. Ieee Access, 7, 124809-124827.
  68. Rasli, A., Tee, M., Lai, Y. L., Tiu, Z. C., & Soon, E. H. (2022, October). Post-COVID-19 strategies for higher education institutions in dealing with unknown and uncertainties. Frontiers in Education, 7, 992063.
  69. Reddy A, B., & Jose, S. (2021). Of access and inclusivity digital divide in online education. arXiv e-prints, arXiv-2107.
  70. Rekha, I. S., Shetty, J., & Basri, S. (2023). Students’ continuance intention to use MOOCs: empirical evidence from India. Education and Information Technologies, 28(4), 4265-4286.
  71. Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
  72. Roca, J.C., Chiu, C.M., & Martínez, F. J. (2006) “Understanding e-learning continuance intention: an extension of the Technology Acceptance Model”. International Journal of Human-Computer Studies, 64(8), 683-696.
  73. Romero-Rodríguez, L. M., Ramírez-Montoya, M. S., & Aguaded, I. (2020). Determining factors in MOOCs completion rates: Application test in energy sustainability courses. Sustainability, 12(7), 2893.
  74. Saleh, S. S., Nat, M., & Aqel, M. (2022). Sustainable adoption of e-learning from the TAM perspective. Sustainability, 14(6), 3690
  75. Shen, Y., Chu, L., Yang, S., Zhang, X., & Yu, Z. (2024). A Systematic Review on Engagement, Motivation, and Performance in MOOCs During the Post-Pandemic Time. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 19(1), 1-21.
  76. Singh, A., Sharma, S., & Paliwal, M. (2021). Adoption intention and effectiveness of digital collaboration platforms for online learning: the Indian students’ perspective. Interactive Technology and Smart Education, 18(4), 493-514.
  77. Song, Z. X., Cheung, M. F., & Prud’Homme, S. (2017). Theoretical frameworks and research methods in the study of MOOC/e-learning behaviors: A theoretical and empirical review. New ecology for education - Communication X learning, 47-65.
  78. Sun, L., Tang, Y., & Zuo, W. (2020). Coronavirus pushes education online. Nature Materials, 19(6), 687-687.
  79. Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia manufacturing, 22, 960-967.
  80. Tao, D., Fu, P., Wang, Y., Zhang, T., & Qu, X. (2019). Key characteristics in designing massive open online courses (MOOCs) for user acceptance: An application of the extended technology acceptance model. Interactive Learning Environments, 1-14
  81. Tawafak, R. M., Malik, S. I., & Alfarsi, G. (2020). Development of framework from adapted TAM with MOOC platform for continuity intention. Development, 29(1), 1681-1691.
  82. Tella, A., Tsabedze, V., Ngoaketsi, J., & Enakrire, R. T. (2021). Perceived usefulness, reputation, and Tutors' advocate as predictors of MOOC utilization by distance learners: Implication on library Services in Distance Learning in Eswatini. Journal of Library & Information Services in Distance Learning, 15(1), 41-67.
  83. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
  84. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
  85. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
  86. Virani, S. R., Saini, J. R., & Sharma, S. (2023). Adoption of massive open online courses (MOOCs) for blended learning: The Indian educators’ perspective. Interactive Learning Environments, 31(2), 1060-1076.
  87. Wang, K., van Hemmen, S. F., & Criado, J. R. (2022). The behavioural intention to use MOOCs by undergraduate students: incorporating TAM with TPB. International Journal of Educational Management, 36(7), 1321-1342.
  88. Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
  89. Wu, B., & Zhang, C. Y. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027e1038.
  90. Yang, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65(5), 1195-1214.