Analysis Of Undergraduate Students’ Behavioral Intentions and Usage Behavior of Online Learning Platforms in Chengdu, Sichuan, China
DOI:
https://doi.org/10.14456/shserj.2023.50Keywords:
Online learning platform, Attitude, Behavior Intention, Use Behavior, Structural Equation ModelingAbstract
Purpose: This study examines the factors affecting behavioral intention and usage behavior of online learning platforms among undergraduate students in Xihua University in Chengdu, Sichuan, China. A conceptual framework is developed through the Theory of Planned Behavior (TPB), the technology acceptance model (TAM) and its extended Model (TAM2), and the unified theory of technology acceptance and use (UTAUT). The researcher determines key variables which are social influence, perceived usefulness, perceived ease of use, attitudes, subjective norms, and perceived behavioral control behavioral intention and usage behavior. Research design, data, and methodology: The target population is 500 participants. The study applied quantitative method to distribute online questionnaires. The sampling method used are purposive and convenience sampling. The data were analyzed by Confirmation factor analysis (CFA) to test the validity and reliability. In addition, structural equation modeling (SEM) model was used to evaluate the hypotheses. Results: The results showed that behavioral intention and use behavior were significantly influenced by social influence, perceived usefulness, perceived ease of use, attitudes, subjective norms, and perceived behavioral control. Conclusions: The findings imply that users’ behavioral intentions are crucial to online learning adoption and suggests that platform designers should fully improve and upgrade online learning platform systems.
References
Ajzen, I., & Fishbein, M. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. https://doi.org/10.2307/2065853
Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888-918. https://doi.org/10.1037/0033-2909.84.5.888
Alaeddin, O., Altounjy, R., Zainudin, Z., & Kamarudin, F. (2018). From physical to digital: investigating consumer behaviour of switching to mobile wallet. Polish Journal of Management Studies, 17(2), 18-30. https://doi.org/10.17512/pjms.2018.17.2.02
Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., Lal, B., & Williams, M. D. (2015). Adoption of Mobile Banking in Jordan: Exploring Demographic Differences on Customers' Perceptions. Open and Big Data Management and Innovation, 9373, 13-23. https://doi.org/10.1007/978-3-319-25013-7_2
Arbuckle, J. (2012). Amos 18 User's Guide (1st ed.) SPSS Incorporated.
Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail and Distribution Management, 45(6), 626-640. https://doi.org/10.1108/ijrdm-08-2016-0144
Bollen, K. A. (1989). Structural Equations with Latent Variables. Wiley, MA.
Brown, A., & Hill, K. (1998). Interviewer Style and Candidate Performance in the IELTS Oral Interview. International English Language Testing System (IELTS) Research Reports.
Calisir, F., Atahan, L., & Saracoglu, M. (2013). Factors affecting social network sites usage on smartphones of students in Turkey (1st ed.). Proceedings of the World Congress on Engineering and Computer Science.
Chatzoglou, P. D., & Vraimaki, E. (2009). Knowledge-sharing behaviour of bank employees in Greece. Business Process Management Journal, 15(2), 245-266. https://doi.org/10.1108/14637150910949470
Chennamaneni, A., Teng, J. T. C., & Raja, M. K. (2012). A unifified model of knowledge sharing behaviours: theoretical development and empirical test. Behaviour & Information Technology, 31(11), 1097-1115.
https://doi.org/10.1080/0144929x.2011.624637
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 6(3),160-175. https://doi.org/10.1016/j.compedu.2012.12.003
Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: an extension of the technology acceptance model. Computers in Human Behavior, 26(6), 1674-1684. https://doi.org/10.1016/j.chb.2010.06.016
Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods (12th ed.). McGraw Hill International Edition.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, 13(3), 319-339. https://doi.org/10.1287/mnsc.35.8.982
Dwivedi, Y., Rana, N., Chen, H., & Williams, M. (2011). A meta-analysis of the unified theory of acceptance and use of technology (UTAUT) (1st ed.). Proceedings of IFIP International Working Conference on Governance and Sustainability in Information Systems-Managing, Current Opinion in Psychology, 36, 13-18. https://doi.org/10.1016/j.copsyc.2020.03.008
Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on its adoption and non-adoption. Journal of Information Technology, 24(1), 11-24. https://doi.org/10.1057/jit.2008.31
Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2015). The acceptance of E-learning as a tool for teaching and learning in Libyan higher education. IPASJ International Journal of Information Technology (IIJIT), 21(4), 1-11.
Fraering, M. S., & Minor, M. (2005). Sense of community: an exploratory study of US consumers of financial services. International Journal of Bank Marketing, 24(5), 284-306. https://doi.org/10.1108/02652320610681738
Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7-28. https://doi.org/10.17705/1cais.00407
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (4th ed.). Pearson College Division.
Hanafizadeh, P., Behboudi, M., Abedini Koshksaray, A., & Jalilvand Shirkhani Tabar, M. (2014). Mobile-banking adoption by Iranian bank clients. Telematics and Informatics, 31(1), 62-78. https://doi.org/10.1016/j.tele.2012.11.001
Hoehle, H., Scornavacca, E., & Huff, S. (2012). Three decades of research on consumer adoption and utilization of electronic banking channels: a literature analysis. Decision Support Systems, 54(1), 122-132. https://doi.org/10.1016/j.dss.2012.04.010
Karaali, D., Gumussoy, C. A., & Calisir, F. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior. 27(1), 343-354.
Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value -based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111-126. https://doi.org/10.1016/j.dss.2005.05.009
Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). Guilford Press.
Kumar, A., Adlakaha, A., & Mukherjee, K. (2018). The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country. International Journal of Bank Marketing, 36(7), 1170-1189. https://doi.org/10.1108/ijbm-04-2017-0077
Li, Q. C., & Wu, M. Y. (2019). Rationality or morality? A comparative study of pro-environmental intentions of local and nonlocal visitors in nature-based destinations. Journal of Destination Marketing & Management, 11, 130-139. https://doi.org/10.1016/j.jdmm.2019.01.003
Liébana -Cabanillas, F., Sánchez -Fernández, J., & Muoz -Leiva, F. (2014). The moderating effect of experience in the adoption of mobile payment tools in virtual social networks: The m-payment acceptance model in virtual social networks (MPAM -VSN). International Journal of Information Management, 34(2), 151-166.
Liu, J. (2017). Research on the mixed teaching mode based on MOOC. J. Teaching Research, 40(5), 65-70.
Mallat, N., Rossi, M., & Tuunainen, V. K. (2009). The impact of use context on mobile services acceptance:The case of mobile ticketing. Information and Management, 46(3), 190-195.
Masa'deh, R. M. T., Tarhini, A., Bany, M. A., & Maqableh, M. (2016). Modeling factors affecting student's usage behavior of E-learning systems in Lebanon. International Journal of Business and Managemen, 11(2), 299-312. https://doi.org/10.5539/ijbm.v11n2p299
Nysveen, H., Pedersen, P. E., & Thorbjornsen, H. (2005). Intentions to use mobile services: antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330-346. https://doi.org/10.1177/0092070305276149
Pedroso, V., Navarro, V., Fleites, B., & Solis, S. (2016). Feeding systems with foliage of Morus alba and sugar cane stalks for fattening rabbits. Technical note, 17(12), 1-7.
Pikkarainen, T., Pikkarainen, K., & Karjaluoto, H. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research. Electronic Networking Applications and Policy, 14(3), 224-235. https://doi.org/10.1108/10662240410542652
Purwanegara, M., Apriningsih, A., & Andika, F. (2014). Snapshot on Indonesia regulation in mobile internet banking users’ attitudes. Procedia-Social and Behavioral Sciences, 115(2), 147-155. https://doi.org/10.1016/j.sbspro.2014.02.423
Sarmento, R., & Costa, V. (2019). Confirmatory Factor Analysis -A Case study. Transportation Research Procedia, 14, 1210-1214.
Schierz, P., Schilke, O., & Wirtz, B. (2010). Understanding customer acceptance of mobile payment services: an empirical analysis. Journal of Electronic Commerce Research and Application, 9(3), 209-216. https://doi.org/10.1016/j.elerap.2009.07.005
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: a literature review, Telematics and Informatics, 32(1),129-142. https://doi.org/10.1016/j.tele.2014.05.003
Shasha, Y., & Leelakasemsant, C. (2022). Influencing Factors of Entrepreneurial Intention among Engineering Students in Sichuan, China. AU-GSB E-JOURNAL, 15(2), 25-36. https://doi.org/10.14456/augsbejr.2022.69
Sica, C., & Ghisi, M. (2007). The Italian Versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric Properties and Discriminant Power. In M.A. Lange (Ed.), Leading - Edge Psychological Tests and Testing Research (pp. 27-50). Nova.
Sivathanu, B. (2019). Adoption of digital payment systems in the era of demonetization in India: an empirical study. Journal of Science and Technology Policy Management, 10(1), 143-171. https://doi.org/10.1108/jstpm-07-2017-0033
Sun, X., & Song, X. (2017). The impact of MOOC on the traditional classroom and the countermeasures. J, Teaching Research, 40(5), 45-49.
Tarhini, A., Hone, K., & Liu, X. (2015a). A cross-cultural examination of the impact of social, organizational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46(4), 739-755. https://doi.org/10.1111/bjet.12169
Taylor, S., & Todd, P. A. (2014). Understanding information technology usage: A test of competing models. Int. J. Information systems research, 6(2), 144-176.
Terzis, V., Moridis, C. N., & Economides, A. A. (2012). How student’s personality traits affect computer-based assessment acceptance: integrating biff with cbaam. Computers in Human Behavior, 28(5), 1985-1996. https://doi.org/10.1016/j.chb.2012.05.019
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., Morris, M., & Davis, G. B. (2003). User acceptance of information technology:toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Wangpipatwong, S., Chutimaskul, W., & Papasratorn, B. (2008). Understanding citizen's continuance intention to use egovernment website: A Composite view of technology acceptance model and computer self-efficacy. The Electronic Journal of e-Government, 6(1),55-64.
Wu, J., & Du, H. (2012). Toward a better understanding of behavioural intention and system usage constructs. European Journal of Information Systems, 21(6), 680-698. https://doi.org/10.1057/ejis.2012.15
Wulandari, N. (2017). Cashless payment in tourism. An application of technology acceptance model. Journal of Environmental Management and Tourism, 8(24), 1550-1553.
Downloads
Published
How to Cite
Issue
Section
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.