Impact of motivation and technology factors to predict satisfaction and continued intentions toward online courses

Authors

  • Qing Wang School of Economics and Management, Huazhong Agricultural University, Wuhan 430072
  • Muhammad Saqib Khan Huazhong University of Science and Technology 1037, Luoyu Road, Wuhan, 430074 https://orcid.org/0000-0002-7722-1018

DOI:

https://doi.org/10.20525/ijrbs.v10i3.1148

Keywords:

Controlled motivation, autonomous motivation, technology acceptance model, perceived satisfaction, continued intention

Abstract

The rapid developments and diffusion of new technologies abruptly changed world dynamics. This study pursued the motivational factors (controlled and autonomous) and technology factors (perceived ease of use and perceived usefulness) to predict the students perceived satisfaction and continued intention toward MOOCs. Using an online survey, this research collected data from 333 students, and analysis performed through PLS-SEM. The findings revealed that controlled motivation positively influenced the perceived satisfaction. However, autonomous motivation positively affected students perceived satisfaction and continued intention toward MOOCs. The technology factors such as PEU strongly impacted PU. Similarly, PU positively impacted students perceived satisfaction and continued intention toward MOOCs. This research guides essential theoretical insights and provides practical guidelines to educational institutions and technologists to develop and implement systems and strategies in online environments.

References

Abdullatif, H., & Velázquez-Iturbide, J. Á. (2020). Relationship between motivations, personality traits and intention to continue using MOOCs. Education and Information Technologies, 25(5), 4417-4435. https://doi.org/10.1007/s10639-020-10161-z

Al-Adwan, A. S. (2020). Investigating the drivers and barriers to MOOCs adoption: The perspective of TAM. Education and Information Technologies, 25(6), 5771-5795. https://doi.org/10.1007/s10639-020-10250-z

Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006

Anderson, T. (2013). Promise and/or peril: MOOCs and open and distance education. Commonwealth of learning, 3, 1-9.

Atique, M., Safeer, A. A., Ullah, A., & Iftikhar, H. (2021). A Study of Impacting Factors on Technology Adoption in the Public Sector of Pakistan. Journal of Contemporary Issues in Business and Government, 27(2), 1281-1302.

Bazelais, P., Doleck, T., & Lemay, D. J. (2018). Investigating the predictive power of TAM: A case study of CEGEP students’ intentions to use online learning technologies. Education and Information Technologies, 23(1), 93-111. https://doi.org/10.1007/s10639-017-9587-0

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921

Chen, C.-C., & Chen, C.-Y. (2018). Exploring the effect of learning styles on learning achievement in a u-Museum. Interactive Learning Environments, 26(5), 664-681. https://doi.org/10.1080/10494820.2017.1385488

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.

Chiu, C.-M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. https://doi.org/10.1016/j.im.2008.02.003

Cigdem, H., & Ozturk, M. (2016). Factors affecting students’ behavioral intention to use LMS at a Turkish post-secondary vocational school. International Review of Research in Open and Distributed Learning, 17(3), 276-295. https://doi.org/10.19173 /irrod l.v17i3.2253

Clow, D. (2013, 2013). MOOCs and the funnel of participation. Proceedings of the third international conference on learning analytics and knowledge,

Conole, G. (2016). MOOCs as disruptive technologies: strategies for enhancing the learner experience and quality of MOOCs. Revista de Educación a Distancia (RED)(50).

Daniel, J., Cano, E. V., & Cervera, M. G. (2015). El futuro de los MOOC: aprendizaje adaptativo o modelo de negocio? RUSC. Universities and Knowledge Society Journal, 12(1), 64-73.

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. https://doi.org/10.1287/mnsc.35.8.982

de Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students' motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218-231. https://doi.org/10.1111/jcal.12130

del Barrio-García, S., Arquero, J. L., & Romero-Frías, E. (2015). Personal learning environments acceptance model: The role of need for cognition, e-learning satisfaction and students' perceptions. Journal of Educational Technology & Society, 18(3), 129-141.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Gameel, B. G., & Wilkins, K. G. (2019). When it comes to MOOCs, where you are from makes a difference. Computers & Education, 136, 49-60. https://doi.org/10.1016/j.compedu.2019.02.014

Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101-107.

Gillet, N., Gagné, M., Sauvagère, S., & Fouquereau, E. (2013). The role of supervisor autonomy support, organizational support, and autonomous and controlled motivation in predicting employees' satisfaction and turnover intentions. European Journal of Work and Organizational Psychology, 22(4), 450-460. https://doi.org/10.1080/1359432X.2012.665228

Gupta Kriti, P. (2019). Investigating the adoption of MOOCs in a developing country: Application of technology-user-environment framework and self-determination theory. Interactive Technology and Smart Education, 17(4), 355-375. https://doi.org/10.1108/ITSE-06-2019-0033

Hair Joseph, F., Risher Jeffrey, J., Sarstedt, M., & Ringle Christian, M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

Hsu, J.-Y., Chen, C.-C., & Ting, P.-F. (2018). Understanding MOOC continuance: An empirical examination of social support theory. Interactive Learning Environments, 26(8), 1100-1118. https://doi.org/10.1080/10494820.2018.1446990

Huang, L., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management, 37(2), 84-91. https://doi.org/10.1016/j.ijinfomgt.2016.12.002

Huanhuan, W., & Xu, L. (2015). Research on technology adoption and promotion strategy of MOOC. 6th IEEE International Conference on Software Engineering and Service Science (ICSESS),

I. Pozón, L., Kalinic, Z., Higueras-Castillo, E., & Liébana-Cabanillas, F. (2019). A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC). Interactive Learning Environments, 28(8), 1003-1021. https://doi.org/10.1080/10494820.2019.1636074

Irma, P.-L., Higueras-Castillo, E., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2020). Perceived user satisfaction and intention to use massive open online courses (MOOCs). Journal of Computing in Higher Education, 1-36. https://doi.org/10.1007/s12528-020-09257-9

Joo, Y. J., So, H.-J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272. https://doi.org/10.1016/j.compe du.2018.01.003

Jungert, T., Landry, R., Joussemet, M., Mageau, G., Gingras, I., & Koestner, R. (2015). Autonomous and controlled motivation for parenting: Associations with parent and child outcomes. Journal of Child and Family Studies, 24(7), 1932-1942. https://doi.org/10.1007/s10826-014-9993-5

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003

Koestner, R., Otis, N., Powers, T. A., Pelletier, L., & Gagnon, H. (2008). Autonomous motivation, controlled motivation, and goal progress. Journal of personality, 76(5), 1201-1230. https://doi.org/10.1111/j.1467-6494.2008.00519.x

Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329. https://doi.org/10.1016/j.compedu.2009.06.014

Lee, J.-W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The internet and higher education, 13(4), 277-283. https://doi.org/10.1016/j.iheduc.2010.08.002

Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The internet and higher education, 29, 40-48. https://doi.org/10.1016/j.iheduc.2015.12.003

Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 105-117.

Magen-Nagar, N., & Cohen, L. (2017). Learning strategies as a mediator for motivation and a sense of achievement among students who study in MOOCs. Education and Information Technologies, 22(3), 1271-1290. https://doi.org/10.1007/s10639-016-9492-y

Mikalef, P., Pappas Ilias, O., & Giannakos, M. (2016). An integrative adoption model of video-based learning. The International Journal of Information and Learning Technology, 33(4), 219-235. https://doi.org/10.1108/IJILT-01-2016-0007

Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374. https://doi.org/10.1016/j.chb.2014.07.044

Nikou, S. A., & Economides, A. A. (2017). Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance. Computers in Human Behavior, 68, 83-95. https://doi.org/10.1016/j.chb.2016.11.020

Ouyang, Y., Tang, C., Rong, W., Zhang, L., Yin, C., & Xiong, Z. (2017). Task-technology fit aware expectation-confirmation model towards understanding of MOOCs continued usage intention. Proceedings of the 50th Hawaii International Conference on System Sciences, Hawaii.

Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D., & Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal Of Marketing, 13(2). https://doi.org/10.5585/remark.v13i2.2717

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American psychologist, 55(1), 68. https://doi.org/10.1037/0003-066X.55.1.68

Saeed Al-Maroof, R., Alhumaid, K., & Salloum, S. (2021). The Continuous Intention to Use E-Learning, from Two Different Perspectives. Education Sciences, 11(1), 2-20. https://doi.org/10.3390/educsci11010006

Safeer, A. A., He, Y., & Abrar, M. (2020). The influence of brand experience on brand authenticity and brand love: an empirical study from Asian consumers’ perspective. Asia Pacific Journal of Marketing and Logistics. https://doi.org/10.1108/APJML-02-2020-0123

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. Handbook of market research, 26(1), 1-40.

Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.

Shahijan, M. K., Rezaei, S., & Amin, M. (2016). International students’ course satisfaction and continuance behavioral intention in higher education setting: an empirical assessment in Malaysia. Asia Pacific Education Review, 17(1), 41-62. https://doi.org/10.1007/s1256 4-015-9410-9

Streukens, S., Wetzels, M., Daryanto, A., & De Ruyter, K. (2010). Analyzing factorial data using PLS: Application in an online complaining context. In Handbook of partial least squares (pp. 567-587). Springer.

Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compe du.2006.11.007.

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.

Teo, T., & Dai, H. M. (2019). The role of time in the acceptance of MOOCs among Chinese university students. Interactive Learning Environments, 1-14. https://doi.org/10.1080/10494820.2019.1674889

Thong, J. Y. L., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of human-computer studies, 64(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.

Wang, Q., Khan, M. S., & Khan, M. K. (2021). Predicting user perceived satisfaction and reuse intentions toward Massive Open Online Courses (MOOCs) in the Covid-19 pandemic: An application of the UTAUTmodel and quality factors. International Journal of Research in Business and Social Science, 10(2), 1-11. https://doi.org/10.20525/ijrbs.v10i2.1045

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. https://doi.org/10.1016/j.chb.2016.10.028

Xu, F. (2015). Research of the MOOC study behavior influencing factors. Proceedings of the international conference on advanced information and communication technology for education, Netherlands.

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. https://doi.org/10.1007/s11423-017-9513-6

Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015, 2015). Understanding student motivation, behaviors and perceptions in MOOCs. Proceedings of the 18th ACM conference on computer supported cooperative work & social computing,

Zhou, M. (2016). Chinese university students' acceptance of MOOCs: A self-determination perspective. Computers & Education, 92, 194-203. https://doi.org/10.1016/j.compedu.2015.10.012

Downloads

Published

2021-05-01

How to Cite

Wang, Q., & Khan, M. S. (2021). Impact of motivation and technology factors to predict satisfaction and continued intentions toward online courses. International Journal of Research in Business and Social Science (2147- 4478), 10(3), 501–513. https://doi.org/10.20525/ijrbs.v10i3.1148

Issue

Section

Learning & Education