A Study on Intention and Behavior of Undergraduates to Use Massive Open Online Courses in Sichuan, China
Article Sidebar
Main Article Content
Abstract
Purpose: Based on COVID-19’s effect in China, this paper explores the influence of behavioral intention and behavior of undergraduates in their use of Massive Open Online Courses (MOOCs) learning in China. The research model is built upon the key constructs, including self-efficacy, perceived usefulness, perceived ease of use, attitude, behavioral intention, subjective norm, and behavior. Research design, data, and methodology: The target population includes 500 undergraduates using MOOCs. This study was conducted using a quantitative method, using a questionnaire. The sampling techniques are judgmental, convenience, and snowball sampling. The content validity was verified by the item-objective congruence (IOC) index, and the reliability test was employed by Cronbach alpha through a pilot test (n=50). In addition, confirmatory factor analysis (CFA) and structural equation modeling (SEM) were analyzed. Results: Self-efficacy has a significant impact on perceived ease of use. Perceived usefulness and perceived ease of use significantly impact attitude. Attitude and subjective norms significantly impact behavioral intention toward behavior. Nevertheless, this study found a non-supported relationship between self-efficacy, perceived usefulness, and perceived ease of use. Conclusions: The findings recognize that most learners are concerned with MOOCs’ efficiency, costless, convenience, and openness, and this has also attracted special attention from educational theorists and academics
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Achenreiner, G., Kleckner, M. J., Knight, P., & Lilly, B. (2019). Student self-efficacy, employee engagement, and community vitality: a collaborative data collection model for regional work force development. Journal of Education and Work, 32(6-7), 614-632.
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
Al-Debei, M. M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54.
Amos, C., Holmes, G., & Strutton, D. (2008). Exploring the relationship between celebrity endorser effects and advertising effectiveness: a quantitative synthesis of effect size. International Journal of Advertising, 27(2), 209-234.
Arteaga Sánchez, R., Duarte Hueros, A., & García Ordaz, M. (2013). E‐learning and the University of Huelva: a study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135-160. https://doi.org/10.1108/10650741311306318
Basar, Z. M., Mansor, A. N., Jamaludin, K. A., & Alias, B. S. (2021). The Effectiveness and Challenges of Online Learning for Secondary School Students – A Case Study. Asian Journal of University Education (AJUE), 17(3), 119-129.
Bhattacherjee, A. (2000). Acceptance of internet applications service: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans, 30(4), 411-420.
Bland, J. M., & Altman, D. G. (1997). Cronbach’s Alpha. British Medical Journal, 314, 572.
http://dx.doi.org/10.1136/bmj.314.7080.572
Choi, H., Kim, Y. C., & Kim, J. W. (2011). Driving factors of post adoption behavior in mobile data services. Journal of Business Research. 64(11), 1212–1217. https://doi.org/10.1016/j.jbusres.2011.06.025
Davis, F. (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
Fatima, J. K., Ghandforoush, P., Khan, M., & Masico, R. D. (2017). Role of innovativeness and self-efficacy in tourism m-learning. Tourism Review, 72(3), 344-355. https://doi.org/10.1108/TR-02-2017-0019.
Feng, D., Xiang, C., Vongurai, R., & Pibulcharoensit, S. (2022). Investigation on Satisfaction and Performance of Online Education Among Fine Arts Major Undergraduates in Chengdu Public Universities. AU-GSB E-JOURNAL, 15(2), 169-177. https://doi.org/10.14456/augsbejr.2022.82
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research (1st ed.). Addison-Wesley.
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/10.2307/3151312
Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280.
Gao, Z., Xiang, P., Lee, A., & Harrison, L. (2008). Self-Efficacy and Outcome Expectancy in Beginning Weight Training Class: Their Relations to Students' Behavioral Intention and Actual Behavior. Research quarterly for exercise and sport, 79(1), 92-100. https://doi.org/10.5641/193250308X13086753543059
Golnaz, R., Zainulabidin, M., Mad Nasir, S., & Eddie Chiew, F. C. (2010). Non-Muslim perception awareness of halal principle and related food products in Malaysia. International Food Research Journal, 17(3), 667-674.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.
Hee, S. P. (2000). Relationships among attitudes and subjective norm: testing the theory of reasoned action across cultures. Communication Studies, 51(2), 162-175.
Hsiao, C.-H., & Tang, K.-Y. (2014). Explaining undergraduates’ behavior intention of e-textbook adoption: Empirical assessment of five theoretical models. Library Hi Tech, 32(1), 139-163. https://doi.org/10.1108/LHT-09-2013-0126
Huang, J., Lin, Y., & Chuang, S. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585-598. https://doi.org/10.1108/02640470710829569
ICEE. (2022, March 30). “Developing online education” Was Written into the 2022 Chinese Prime Minister's Government Work Report. http://www.icee-unesco.org/news/99
Jain, G., Singh, H., Chaturvedi, K., & Rakesh, S. (2020). Blockchain in logistics industry: in fizz customer trust or not. Journal of Enterprise Information Management, 33(3), 541-558 https://doi.org/10.1108/JEIM-06-2018-0142
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404-416. https://doi.org/10.1016/j.ijmedinf.2008.12.005
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). The Guilford Press
Lee, Y. (2006). An empirical investigation into factors influencing the adoption of an e‐learning system. Online Information Review, 30(5), 517-541. https://doi.org/10.1108/14684520610706406
Liaw, S.-S., Huang, H.-M., & Chen, G.-D. (2007). Surveying instructor and learner attitudes toward E-learning. Computers & Education, 49(4), 1066-1080. https://doi.org/10.1016/j.compedu.2006.01.001
Lin, W. (2008). Construction of on‐line consumer behavior models: a comparative study of industries in Taiwan. International Journal of Commerce and Management, 18(2), 123-149. https://doi.org/10.1108/10569210810895221
Maity, M., Bagchi, K., Shah, A., & Misra, A. (2019). Explaining normative behavior in information technology use. Information Technology & People, 32(1), 94-117. https://doi.org/10.1108/ITP-11-2017-0384
Moes, A., & Vliet, H. V. (2017). The online appeal of the physical shop: how a physical store can benefit from a virtual representation. Heliyon, 3(6), e00336. https://doi.org/ 10.1016/j.heliyon.2017
Ozgen, O., & Kurt, S. D. K. (2013). Purchasing behavior of Islamic brands: an experimental research [Paper Presentation]. 42nd Annual Conference of EMAC, European Marketing Academy, Istanbul.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.
Pew Research Center. (2019, October 28). The internet will continue to make life better. https://www.pewresearch.org/internet/2019/10/28/4-the-internet-will-continue-to-make-life-better/
Reed, A., Forehand, M. R., Puntoni, S., & Warlop, L. (2012). Identity based consumer behavior. International Journal of Research in Marketing, 29(4), 310-321.
Schiano di Pepe, F. (2021, March 27). The evolution of online education: from MOOC to Cohort-Based Courses. Teachfloor. https://www.teachfloor.com/blog/evolution-online-education-cohort-based-courses
Shao, Z. (2018). Examining the impact mechanism of social psychological motivations on individuals’ continuance intention of MOOCs: The moderating effect of gender. Internet Research, 28(1), 232-250. https://doi.org/10.1108/IntR-11-2016-0335
Siriphat, P. (2016). An empirical study of the relationship between job demand-control-support model, self-efficacy, burnout, informal learning, and job performance: A case of employees in the Thai banking sector. AU-GSB e-Journal, 10(1), 195–213.
Smith, P. J., Smith, P. J., Murphy, K. L., Murphy, K. L., Mahoney, S. E., & Mahoney, S. E. (2003). Towards Identifying Factors Underlying Readiness for Online Learning: An Exploratory Study. Distance Education, 24, 57-67.
http://dx.doi.org/10.1080/01587910303043
Soper, D. S. (n.d.). A-priori Sample Size Calculator for Structural Equation Models [Software]. www.danielsoper.com/statcalc/default.aspx
Stevens, J. P. (1992). Applied multivariate statistics for the social sciences (2nd ed.). Erlbaum.
Taylor, D. G., & Strutton, D. (2010). Has e-marketing come of age? Modeling historical influences on post-adoption era internet consumer behaviors. Journal of Business Research, 63(9/10), 950-956. https://doi.org/10.1016/j.jbusres.2009.01.018
Teo, T., & Beng Lee, C. (2010). Explaining the intention to use technology among student teachers: An application of the Theory of Planned Behavior (TPB). Campus-Wide Information Systems, 27(2), 60-67. https://doi.org/10.1108/10650741011033035
tom Dieck, M. C., Jung, T. H., Kim, W. G., & Moon, Y. (2017). Hotel guests’ social media acceptance in luxury hotels. International Journal of Contemporary Hospitality Management, 29(1), 530-550. https://doi.org/10.1108/IJCHM-10-2015-0552
Tomporowski, P. D. (2003). The psychology of skill: A life-span approach. Praeger.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315. http://dx.doi.org/10.1111/j.1540-5915.2008.00192.x
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
Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of internet banking: an empirical study. International Journal Service Industry Management, 14(5), 501-519.
White, K., Macdonnell, R., & Dahl, D. W. (2011). It’s the mind-set that matters: the role of construal level and message framing in influencing consumer efficacy and conservation behaviors. Journal of Marketing Research, 48(3), 472-485.
Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wear able devices: an extended perspective of perceived value. Telematic sand Informatics, 33(2), 256-269. https://doi.org/ 10.1016/j.tele.2015.08.007