Factors Impacting the Usage Intention of Learning Management System in Higher Education
Article Sidebar
Main Article Content
Abstract
Purpose: The purpose of this research is to examine the factors impacting the usage intention of the Learning Management System (LMS) in higher education of Sichuan, China. Research design, data and methodology: Sample data was collected from target population by using quantitative method and questionnaire as a tool. Before distributing the questionnaire, Item-Objective Congruence (IOC) and pilot test of Cronbach's Alpha were adopted to test the content validity and reliability. Data was analyzed by utilizing Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate model’s goodness of fit and confirm the causal relationship among variables for hypothesis testing. Results: The study has found that the research conceptual model was able to predict and explain the behavioral intention (BI) to use LMS in higher educations. Perceived Usefulness (PU) and Attitude Towards use (ATU) are two key predictors and antecedents of BI to use LMS. Conclusions: Eight hypotheses proposed were proven to fulfill research objectives. This study suggested that developers of LMS course and management of higher education institutions should focus on improving the quality factors of LMS for students to perceive the system as useful, and would further formulate favorable attitude and behavioral intention toward using LMS.
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
Agag, G., & El-Masry, A. (2016). Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Computers in Human Behavior, 60, 97–111. https://doi.org/10.1016/j.chb.2016.02.038
Ahn T., Ryu, S., Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information Management, 44(3), 263-275.
Ajzen, I. (1989). Attitude structure and behavior. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp. 241–274). Lawrence Erlbaum Associates, Inc.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall.
Akroush, M.N. & Al-Debei, M.M. (2015). An integrated model of factors affecting consumer attitudes towards online shopping. Business Process Management Journal, 21(6), 1353-1376. https://doi.org/10.1108/BPMJ-02-2015-0022
Al-Handhali, B.A.S, Al-Rasbi, A.T.N., & Sherimon, P.C. (2020). Advantages and disadvantages of learning management system (LMS) at AOU Oman. International Journal of Technology, 1(2).
Al-Mamary, Y.H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen, Mediterranean Journal of Social Sciences, 6(4), 268-273.
Al-Omairi, L., Al-Samarraie, H., Alzahrani, A.I., & Alalwan, N. (2021). Students' intention to adopt e-government learning services: a developing country perspective. Library Hi Tech, 39(1), 308-334. https://doi.org/10.1108/LHT-02-2020-0034
Alghamdi, S.R., & Bayaga, A. (2016). Use and attitude towards learning management systems (LMS) in Saudi Arabian Universities. Eurasia Journal of Mathematics, Science and Technology Education, 12(9), 2309-2330.
Alias, N.A., & Zainuddin, A.M. (2005) Innovation for better teaching and learning: Adopting the learning management system. Malaysian Online Journal of Instructional Technology, 2, 27-40.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
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
Awang, Z. (2012). Structural equation modeling using AMOS graphic. Penerbit Universiti Teknologi MARA.
Belanger, F., Hiller, J.S., & Smith, W. (2002). Trustworthiness inelectronic commerce: The role of privacy, security, and site attributes. The Journal of Strategic Information Systems, 11(3-4), 245-270. https://doi.org/10.1016/S0963-8687(02)00018-5
Benjangjaru, B., & Vongurai, R. (2018). Behavioral intention of Bangkokians to adopt mobile payment services by type of users. AU-GSB e-Journal, 11(1), 34-46. http://www.assumptionjournal.au.edu/index.php/AU-GSB/article/view/3299
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805-825.
Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 30(4), 411-420.
Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning: A combination of theory of reasoned action and technology acceptance model. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191. https://doi.org/10.1108/JRIT-03-2017-0004
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge Taylor & Francis Group.
Cai, S., & Jun, M. (2003). Internet users’ perceptions of online service quality: a comparison of online buyers and information searchers. Managing Service Quality, 13(6), 504-19.
Camarero, C., Rodríguez, J., & San José, R. (2012). An exploratory study of online forums as a collaborative learning tool. Online Information Review, 36(4), 568-586. https://doi.org/10.1108/14684521211254077
Cao, M., Zhang, Q., & Seydel, J. (2005). B2C e-commerce web site quality: an empirical examination. Industrial Management & Data Systems, 105(5), 645-661.
Chawla, D. & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India-A empirical study. International Journal of Bank Marketing, 37(7), 1590-1618.
Cho, V., Cheng, T.C.E., & Lai, W.M.J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216-27.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.
Davis, F.D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.
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.
DeLone, W.H., & McLean, E.R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.
Drennan, J., Kennedy, J., & Pisarki, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331-338. https://doi.org/10.3200/JOER.98.6.331-338
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. 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.
Gefen, D., & Heart, T.H. (2006). On the need to include national culture as a central issue in e-commerce trust beliefs. Journal of Global Information Management, 14(4), 1-30.
Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90.
Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing & Logistics, 26(2), 211-231.
George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and Reference. 11.0 Update (4th ed.). Allyn & Bacon.
Hair, J.F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis. Prentice-Hall.
Hair, J.F., Anderson, R.E., Tatham, R.L., & Black, W.C. (2010). Multivariate Data Analysis (6th ed.). Prentice Hall.
Hanover Research. (2015, February 11). Hanover’s 2014 Research Themes in Higher Education. https://www.hanoverresearch.com/insights-blog/hanovers-2014-research-themes-in-higher-education/
Harris, L.C., & Goode, M.M.H. (2004). Online servicescapes, trust, and purchase intentions. Journal of Services Marketing, 24(3), 230-243.
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model of measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959-10966.
Hill, P. (2012, November 1). Online Education Delivery Models: A Descriptive View. Educause. https://er.educause.edu/articles/2012/11/online-educational-delivery-models--a-descriptive-view
Hoyle, R.H. (2011). Structural equation modeling for social and personality psychology. Sage.
Jaiyeoba, O.O., & Iloanya, J. (2019). E-learning in tertiary institutions in Botswana: apathy to adoption. International Journal of Information and Learning Technology, 36(2), 157-168. https://doi.org/10.1108/IJILT-05-2018-0058
Khan, R.A., & Ullah, H.Q. (2021). Adoption of LMS in Higher Educational Institutions of the Middle East. Springer.
Killingsworth, B., Xue, Y., & Liu, Y. (2016). Factors influencing knowledge sharing among global virtual teams. Team Performance Management, 22(5/6), 284-300. https://doi.org/10.1108/TPM-10-2015-0042
Kim, E., & Tadisina, S. (2007). A model of customers' trust in e-businesses: Micro-level inter-party trust formation. Journal of Computer Information Systems, 48(1), 88-104.
Kim, T.G., Lee, J.H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: an extended technology acceptance model. Tourism Management, 29(3), 500-513.
Lao, M., & Pupat, N. (2020). Comparison of factors influencing Chinese people in Bangkok intention to use two online payment applications in Thailand. AU-GSB e-Journal, 12(2),74-81. http://www.assumptionjournal.au.edu/index.php/AU-GSB/article/view/4499
Lau, S.-H., & Woods, P.C. (2008). An investigation of user perceptions and attitudes towards learning objects. British Journal of Educational Technology, 39(4), 685-699.
Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: theories and results. Computers & Education, 53(4), 1320-9.
Lee, D., Moon, J., Kim, Y.-J., Yi, M.Y. (2015). Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty. Information & Management, 52(3), 295-304.
Lee, G., & Lin, H. (2005). Customer perceptions of e-service quality in online shopping. International Journal of Retail & Distribution Management, 33(2), 161-76.
Lee, J.-W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The Internet and Higher Education, 13(4), 277-83.
Lee, M. (2009). Factors influencing the adoption of Internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141. https://doi.org/10.1016/j.elerap.2008.11.006
Lin, C., & Lin, M. (2019). The determinants of using cloud supply chain adoption. Industrial Management & Data Systems,119(2), 351-366.
Lin, H.-F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119-138.
Lin, H.-F. (2013). The effect of absorptive capacity perceptions on the context‐aware ubiquitous learning acceptance. Campus-Wide Information Systems, 30(4), 249-265. https://doi.org/10.1108/CWIS-09-2012-0031
Lin, H.-F. (2015). The impact of company-dependent and company-independent information sources on organizational attractiveness perceptions. Journal of Management Development, 34(8), 941-959.
Moczynski, A. (2021). Our tops 5 advantages and disadvantages of online learning. EasyLMS. https://www.easy-lms.com/knowledge-center/lms-knowledge-center/advantages-and-disadvantages-of-online-learning/item12529
Nagy, J.T. (2016). Using learning management systems in business and economics studies in Hungarian higher education. Education and Information Technologies, 21(4), 1360-2357.
Ngai, E.W.T., Poon, J.K.L., & Chan, Y.H.C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267.
Nichols, M. (2003). A theory of eLearning. Journal of Educational Technology & Society, 6(2), 1-10.
Oertzen, A.S., & Schröder, G.O. (2019). Achieving continued usage in online banking: a post-adoption study. International Journal of Bank Marketing, 37(6), 1394-1418.
Ozok, A.A., Wu, H., Garrido, M., Pronovost, P.J., & Gurses, A.P. (2014). Usability and perceived usefulness of personal health records for preventive health care: A case study focusing on patients' and primary care providers' perspectives. Applied Ergonomics, 45(3), 613-628. https://doi.org/10.1016/j.apergo.2013.09.005