The Perception and Behavioral Intention Toward MOOCs: Undergraduates in China

Authors

DOI:

https://doi.org/10.19173/irrodl.v24i1.6677

Keywords:

MOOCs, theory of planned behavior, technology acceptance model, TAM-TPB

Abstract

This study incorporated the technology acceptance model (TAM) and theory of planned behavior (TPB) to interpret students’ perception of MOOCs. This study was based on a survey questionnaire; all 525 respondents were undergraduates in China. A five-point Likert scale was used to collect data in order to measure relationships among the constructs of perceived usefulness (PU), perceived ease of use (PEOU), attitude (ATT), subjective norms (SN), perceived behavioral control (PBC), and behavioral control (BI). The results showed that the research model that incorporated TAM and TPB provided both desirable fit and validity, and all the proposed hypotheses were positively supported. Compared with ATT and SN, PBC had a much stronger impact than did BI. This study and its findings provided educators and MOOC providers with managerial implications as well as suggestions for designing future MOC offerings.

Author Biography

Kai Wang, College of Entrepreneurship, Zhejiang University of Finance and Economics, Hangzhou, China

Kai Wang obtained his Ph.D degree at the Universitat Autònoma de Barcelona. He received his master degree at the Universitat Autònoma de Barcelona in Applied Research in Economics and Business. His research interests are MOOCs, marketing, consumer behaviour and business organization.

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

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Alegre, J., & Chiva, R. (2013). Linking entrepreneurial orientation and firm performance: The role of organizational learning capability and innovation performance. Journal of Small Business Management, 51(4), 491–507. https://doi.org/10.1111/jsbm.12005

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. https://doi.org/10.1186/s41039-022-00181-3

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327

Blanco, Á. F, Echaluce, M. L. S., & Peñalvo, F. J. G. (2016). From massive access to cooperation: Lessons learned and proven results of a hybrid xMOOC/cMOOC pedagogical approach to MOOCs. International Journal of Educational Technology in Higher Education, 13(1), 1–13. https://doi.org/10.1186/s41239-016-0024-z

Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6. https://doi.org/10.3389/fpubh.2018.00149

Chin, W. W., Johnson, N., & Schwarz, A. (2008). A fast form approach to measuring technology acceptance and other constructs. MIS Quarterly, 32(4), 687–703. https://doi.org/10.2307/25148867

Choe, J. Y., Kim, J. J., & Hwang, J. (2021). Innovative marketing strategies for the successful construction of drone food delivery services: Merging TAM with TPB. Journal of Travel & Tourism Marketing, 38(1), 16–30. https://doi.org/10.1080/10548408.2020.1862023

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555

Cronbach, L. J., & Thorndike, R. L. (1971). Test Validation. In R. Thorndike (Ed.), Educational Measurement (2nd ed., p. 443). Washington DC: American Council on Education.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3) 319–340. https://doi.org/10.2307/249008

Duan, P. (2021). The social presence of online education: How MOOC platforms in China cope with collective trauma during COVID-19. Asian Journal of Communication, 31(5), 436–451. https://doi.org/10.1080/01292986.2021.1941152

Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press. https://psycnet.apa.org/record/1992-98610-000

Fishbein, M., & Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.

Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452. https://doi.org/10.1177/002224378201900406

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

Geisser, S. (1975). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101

Glavee-Geo, R., Shaikh, A. A., & Karjaluoto, H. (2017). Mobile banking services adoption in Pakistan: Are there gender differences? International Journal of Bank Marketing, 35(7). https://doi.org/10.1108/IJBM-09-2015-0142

Goel, P., Raj, S., Garg, A., Singh, S., & Gupta, S. (2022). Peeping in the minds of MOOCs instructors: Using fuzzy approach to understand the motivational factors. Online Information Review. Preprint. https://doi.org/10.1108/OIR-04-2021-0205

Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to m-learning acceptance among university students: An integrated model of TPB and TAM. International Review of Research in Open and Distributed Learning, 20(3). https://doi.org/10.19173/irrodl.v20i4.4061CopiedA

Hair, J. F., Hult, G. T., Ringle, C. M., Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM; 2nd ed.). Sage.

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. https://doi.org/10.1007/s11747-014-0403-8

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing. https://doi.org/10.1108/S1474-7979(2009)0000020014

Hollands, F. M., & Tirthali, D. (2014). MOOCs: Expectations and reality. Center for Benefit-Cost Studies of Education, Teachers College, Columbia University. https://www.researchgate.net/publication/271841177_MOOCs_Expectations_and_reality

Hossain, M. N., Hossain, M. Y., Bao, Y., Kumar, N., & Hoque, M. R. (2022). A proposed model to design MOOCs through the lens of addressing graduate skill gap. Higher Education, Skills and Work-Based Learning. Preprint. https://doi.org/10.1108/HESWBL-04-2021-0070

Hossain, M. N., Talukder, M. S., Khayer, A., & Bao, Y. (2020). Investigating the factors driving adult learners’ continuous intention to use M-learning application: A fuzzy-set analysis. Journal of Research in Innovative Teaching & Learning, 14(2), 245–270. https://doi.org/10.1108/JRIT-09-2019-0071

Jang, J., Ko, Y., Shin, W. S., & Han, I. (2021). Augmented reality and virtual reality for learning: An examination using an extended technology acceptance model. IEEE Access, 9, 6798–6809. https://doi.org/10.1109/ACCESS.2020.3048708

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101

Kock, F., Berbekova, A., & Assaf, A. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330. https://doi.org/10.1016/j.tourman.2021.104330

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), 13. https://scholarworks.utrgv.edu/is_fac/27/

Lung-Guang, N. (2019). Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model. Computers & Education, 134, 50–62. https://doi.org/10.1016/j.compedu.2019.02.004

Malik, S., Taqi, M., Martins, J. M., Mata, M. N., Pereira, J. M., & Abreu, A. (2021). Exploring the relationship between communication and success of construction projects: The mediating role of conflict. Sustainability, 13(8), 4513. https://doi.org/10.3390/su13084513

Menon, A., Bharadwaj, S. G., & Howell, R. (1996). The quality and effectiveness of marketing strategy: Effects of functional and dysfunctional conflict in intraorganizational relationships. Journal of the Academy of Marketing Science, 24(4), 299. https://doi.org/10.1177/0092070396244002

Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. Journal of Online Learning and Teaching, 9(2), 149–159. http://jolt.merlot.org/vol9no2/milligan_0613.htm

Moon, S. J. (2021). Investigating beliefs, attitudes, and intentions regarding green restaurant patronage: An application of the extended theory of planned behavior with moderating effects of gender and age. International Journal of Hospitality Management, 92, 102727. https://doi.org/10.1016/j.ijhm.2020.102727

Obaid, T. (2021). Predicting mobile banking adoption: An integration of TAM and TPB with trust and perceived risk. Retrieved September 23, 2022 from http://dx.doi.org/10.2139/ssrn.3761669

Ossiannilsson, E., Altinay, F., & Altinay, Z. (2016). MOOCs as change agents to boost innovation in higher education learning arenas. Education Sciences, 6(3), 25. https://doi.org/10.3390/educsci6030025

Padilha, J. M., Machado, P. P., Ribeiro, A. L., Ribeiro, R., Vieira, F., & Costa, P. (2021). Easiness, usefulness and intention to use a MOOC in nursing. Nurse Education Today, 97, 104705. https://doi.org/10.1016/j.nedt.2020.104705

Qiu, L., Liu, Y., Hu, Q., & Liu, Y. (2019). Student dropout prediction in massive open online courses by convolutional neural networks. Soft Computing, 23(20), 10287–10301. https://doi.org/10.1007/s00500-018-3581-3

Raja, M. A. S., & Kallarakal, T. K. (2021). COVID-19 and students perception about MOOCs: A case of Indian higher educational institutions. Interactive Technology and Smart Education, 18(3). https://doi.org/10.1108/ITSE-07-2020-0106

Ru, X., Qin, H., & Wang, S. (2019). Young people’s behaviour intentions towards reducing PM2.5 in China: Extending the theory of planned behaviour. Resources, Conservation and Recycling, 141, 99–108. https://doi.org/10.1016/j.resconrec.2018.10.019

Si, H., Shi, J. G., Tang, D., Wu, G., & Lan, J. (2020). Understanding intention and behavior toward sustainable usage of bike sharing by extending the theory of planned behavior. Resources, Conservation and Recycling, 152, 104513. https://doi.org/10.1016/j.resconrec.2019.104513

Sun, Y., Ni, L., Zhao, Y., Shen, X. L., & Wang, N. (2019). Understanding students’ engagement in MOOCs: An integration of self‐determination theory and theory of relationship quality. British Journal of Educational Technology, 50(6), 3156–3174. https://doi.org/10.1111/bjet.12724

Taherdoost, H. (2016). Sampling methods in research methodology: How to choose a sampling technique for research. Retrieved September 23, 2022 from http://dx.doi.org/10.2139/ssrn.3205035

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, 30(5) 882–895. https://doi.org/10.1080/10494820.2019.1695214

Unal, E., & Uzun, A. M. (2021). Understanding university students’ behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619–637. https://doi.org/10.1111/bjet.13046

Venkatesh, V., & Goyal, S. (2010). Expectation disconfirmation and technology adoption: Polynomial modeling and response surface analysis. MIS Quarterly, 34(2), 281–303. https://doi.org/10.2307/20721428

Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two‐stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x

Villasenor, J. A., & Estrada, E. G. (2009). A generalization of Shapiro-Wilk’s test for multivariate normality. Communications in Statistics—Theory and Methods, 38(11), 1870–1883. https://doi.org/10.1080/03610920802474465

Viner, R. M., Russell, S. J., Croker, H., Packer, J., Ward, J., Stansfield, C., Mytton, O., Bonel, C., & Booy, R. (2020). School closure and management practices during coronavirus outbreaks including COVID-19: A rapid systematic review. The Lancet Child & Adolescent Health, 4(5), 397–404. https://doi.org/10.1016/S2352-4642(20)30095-X

Wang, Y., Dong, C., & Zhang, X. (2020). Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB. Computer Applications in Engineering Education, 28(6), 1421–1433. https://doi.org/10.1002/cae.22310

Yang, H., & Lee, H. (2022). How does the perceived physical risk of COVID-19 affect sharing economy services? Current Issues in Tourism, 25(7), 1046–1062. https://doi.org/10.1080/13683500.2022.2035700

Yang, H. H., & Su, C. H. (2017). Learner behaviour in a MOOC practice-oriented course: In empirical study integrating TAM and TPB. International Review of Research in Open and Distributed Learning, 18(5), 35–63. https://doi.org/10.19173/irrodl.v18i5.2991

Zhang, J. (2016). Can MOOCs be interesting to students? An experimental investigation from regulatory focus perspective. Computers & Education, 95, 340–351. https://doi.org/10.1016/j.compedu.2016.02.003

Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015). Understanding student motivation, behaviors and perceptions in MOOCs. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 1882–1895). https://doi.org/10.1145/2675133.2675217

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

Published

2023-02-01

How to Cite

Wang, K. (2023). The Perception and Behavioral Intention Toward MOOCs: Undergraduates in China. The International Review of Research in Open and Distributed Learning, 24(1), 22–46. https://doi.org/10.19173/irrodl.v24i1.6677

Issue

Section

Research Articles