• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 8, Pages: 590-597

Original Article

Direct Test Effect of Disruptive Technology Acceptance Model (DTAM) on Massive Online Open Courses (MOOCS) Learners’ Satisfaction

Received Date:27 October 2022, Accepted Date:16 January 2023, Published Date:02 March 2023


Objectives: Higher education learners paid attention to MOOCs in their relevant fields to enhance their skills and knowledge. The purpose of the current study is to find the MOOCs’ disruptive technological dimensions of learners’ attitudes, learners’ usage behavior, and learners’ satisfaction. Methodology: The current study adopted a descriptive research design, it is a cross-sectional study, with 384 MOOCs learners included in a questionnaire survey during the months of June to August 2022 in the Tamil Nadu. For Data analysis, Frequency statistics, the Kruskal Wallis test, and PLS-SEM were employed. Findings: Kruskal Wallis test revealed that there is no mean significant difference between gender, education stream, MOOC preference, and MOOC course mode on attitude to use MOOC, usage behavior, and learners’ satisfaction. PLS-SEM results revealed that there exists a positive significant relationship between the dimensions of disruptive technology (perceived usefulness, perceived ease of use, reliability, portability, and economic value) on the attitude, usage behavior, and learners’ satisfaction. However, there is no significant relationship (t- statistics with 0.783) found between the perceived ease of use dimension of disruptive technology on user behavior. Managerial implications: MOOC service providers need to concentrate on DT dimensions, which affect users’ attitudes, usage behavior, and satisfaction. Novelty: The current study introduces the disruptive technological dimensions with the existing TAM model, it helps Edtech companies, higher education institutions and MOOC service providers to understand the learners’ usage behavior and the expectations of disruptive technology elements in MOOCs. The DT dimensions introduced in the present study will have adopted by future researchers to make the relationship between the emerging DTs and user satisfaction in the different segments and markets.

Keywords: Disruptive technology; Higher education; MOOCs; Online learning; Behaviour


  1. Gejendhiran S, Anicia SA, Vignesh S, Kalaimani M. Disruptive Technologies - A promising key for Sustainable Future Education. Procedia Computer Science. 2020;172:843–847. Available from: https://doi.org/10.1016/j.procs.2020.05.121
  2. Almarzooq ZI, Lopes M, Kochar A. Virtual learning during the COVID-19 pandemic: a disruptive technology in graduate medical education. Journal of the American College of Cardiology. 2020;75(20):2635–2638. Available from: https://doi.org/10.1016/j.jacc.2020.04.015
  3. Siddhpura A, Siddhpura M. Current state of research in application of disruptive technologies in engineering education. Procedia Computer Science. 2020;172:494–501. Available from: https://doi.org/10.1016/j.procs.2020.05.163
  4. Alhashmi SF, Salloum SA, Mhamdi C. Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model. International Journal of Information Technology and Language Studies. 2019;3(3):27–42. Available from: https://journals.sfu.ca/ijitls/index.php/ijitls/article/view/107
  5. Al-Rahmi WM, Yahaya N, Alamri MM, Alyoussef IY, Al-Rahmi AM, Kamin YB. Integrating innovation diffusion theory with technology acceptance model: supporting students’ attitude towards using a massive open online courses (MOOCs) systems. Interactive Learning Environments. 2021;29(8):1380–1392. Available from: https://doi.org/10.1080/10494820.2019.1629599
  6. Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al. Clinical Characteristics of Covid-19 in New York City. New England Journal of Medicine. 2020;382(24):2372–2374. Available from: https://doi.org/10.1056/NEJMc2010419
  7. Panagiotarou A, Stamatiou YC, Pierrakeas C, Kameas A. Gamification Acceptance for Learners with Different E-Skills. International Journal of Learning, Teaching and Educational Research. 2020;19(2):263–278. Available from: https://doi.org/10.26803/ijlter.19.2.16
  8. Salloum SA, Alhamad AQM, Al-Emran M, Monem AA, Shaalan K. Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model. IEEE Access. 2019;7:128445–128462. Available from: https://doi.org/10.1109/ACCESS.2019.2939467
  9. Al-Maatouk Q, Othman MS, Aldraiweesh A, Alturki U, Al-Rahmi WM, Aljeraiwi AA. Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia. IEEE Access. 2020;8:78427–78440. Available from: https://doi.org/10.1109/ACCESS.2020.2990420


© 2023 Priyadarshini et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.