Influencers of the Postgraduate Students’ Continuance Intention to Use E-learning at a Public University in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2024.45Keywords:
E-Learning, Service Quality, Information Quality, Satisfaction, Continuance IntentionAbstract
Purpose: This study investigates how students intend to continue using e-learning at a public university in Chengdu, China. The conceptual framework of the study was built using the Technology Acceptance Model (TAM), the Information System Success Model (ISSM), and the Expectation-Confirmation Model (ECM). Computer self-efficacy, system quality, information quality, service quality, perceived usefulness, satisfaction, and continuance intention were examined their effects on continuance intention to use the e-learning platforms. Research design, data, and methodology: The data were collected from 492 postgraduate students from Xihua University. The researcher used a quantitative survey approach by distributing online questionnaires. The index of item-objective congruence (IOC) was applied and a pilot test (n=50) were conducted to evaluate the reliability using Cronbach's Alpha coefficient. Confirmatory factor analysis and structural equation modeling were employed in this study as statistical analysis tools to assess the data, the validity, reliability, factor loadings, and the path coefficient. Results: The data analysis showed that perceived usefulness had the strongest direct influence on continuance intention, consistent with the entire hypothesis. Conclusions: Administrators and educators should closely examine the variables influencing students’ intention to use e-learning platforms. They should think about improving relevant teaching strategies going forward based on the findings of this study.
References
Abbas, T. (2016). Social factors affecting students’ acceptance of e-learning environments in developing and developed countries: A structural equation modeling approach. Journal of Hospitality and Tourism Technology, 7(2), 200-212. https://doi.org/10.1108/jhtt-11-2015-0042
Agarwal, R., Sambamurthy, V., & Stair, R. (2000). Research Report: The Evolving Relationship Between General and Specific Computer Self-Efficacy--An Empirical Assessment. Information Systems Research, 11(4), 418-430. https://doi.org/10.1287/isre.11.4.418.11876
Al-ammari, J., & Hamad, S. (2008). Factors influencing the adoption of e-learning at UOB [Paper presentation]. International Arab Conference on Information Technology, Ajman, UAE.
Aldholay, A. H., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A. H. (2018). An extension of Delone and McLean IS success model with self-efficacy. Campus-wide Information Systems, 35(4), 285-304. https://doi.org/10.1108/ijilt-11-2017-0116
Almazán, D. A., Tovar, Y. S., & Quintero, J. M. M. (2017). Influence of information systems on organizational results. Contaduría Y Administración, 62(2), 321-338.
Bentler, P. M., & Bonett, D. C. (1980). Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychological Bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588
Beran, T. N., & Violato, C. (2010). Structural equation modeling in medical research: a primer. BMC research notes, 3(1), 1-10.
Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation. Psychometrika, 50(2), 229-242. https://doi.org/10.1007/bf02294248
Brown, S. P., & Chin, W. W. (2004). Satisfying and Retaining Customers through Independent Service Representatives. Decision Sciences, 35(3), 527-550. https://doi.org/10.1111/j.0011-7315.2004.02534.x
Byrne, B. M. (2001). Structural Equation Modeling With AMOS, EQS, and LISREL: Comparative Approaches to Testing for the Factorial Validity of a Measuring Instrument. International Journal of Testing, 1(1), 55-86. https://doi.org/10.1207/s15327574ijt0101_4
Chang, C. (2013). Exploring the determinants of e-learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55. https://doi.org/10.1108/01435121311298261
Chau, P. Y. K., & Hu, P. J. H. (2001). Information Technology Acceptance by Individual Professional: A Model Comparison Approach. Decision Sciences, 32, 699-719.
https://doi.org/10.1111/j.1540-5915.2001.tb00978.x
Chen, C.-C., Lee, C.-H., & Hsiao, K.-L. (2018). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan: Effects of interactivity and openness. Library Hi Tech, 36(4), 705-719. https://doi.org/10.1108/lht-11-2016-0129
Cheng, Y.-M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390.
https://doi.org/10.1108/10662241211235699
Cheng, Y.-M. (2014). Extending the expectation-confirmation model with quality and flow to explore nurses continued blended e-learning intention. Information Technology & People, 27(3), 230-258. https://doi.org/10.1108/itp-01-2013-0024
Cheng, Y.-M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact? Education + Training, 61(4), 480-499. https://doi.org/10.1108/et-09-2018-0203
Cheng, Y.-M. (2021). Can gamification and interface design aesthetics lead to MOOCs' success? Education + Training, 63(9), 1346-1375. https://doi.org/10.1108/et-09-2020-0278
Chiu, C.-M., Chiu, C.-S., & Chang, H.-C. (2007). Examining the integrated influence of fairness and quality on learners' satisfaction and Web-based learning continuance intention. Information Systems Journal, 17(3), 271-287.
https://doi.org/10.1111/j.1365-2575.2007.00238.x
Chopra, G., Madan, P., Jaisingh, P., & Bhaskar, P. (2019). Effectiveness of e-learning portal from students’ perspective: A structural equation model (SEM) approach. Interactive Technology and Smart Education, 16(2), 94-116. https://doi.org/10.1108/itse-05-2018-002
Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2017). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273-290.
https://doi.org/10.1016/j.compedu.2017.12.001
Cooper, D., & Schindler, P. (2010). Business Research Methods (11th Ed.). McGraw-Hill/Irwin.
Darawong, C., & Widayati, A. (2021). Improving student satisfaction and learning outcomes with service quality of online courses: evidence from Thai and Indonesian higher education institutions. Journal of Applied Research in Higher Education, 14(4), 1245-1259.
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.
Fan, X., Duangekanong, S., & Xu, M. (2021). Factors Affecting College Students’ Intention to Use English U-learning in Sichuan, China. AU-GSB E-JOURNAL, 14(2), 118-129. https://doi.org/10.14456/augsbejr.2021.20
Gong, M., Xu, Y., & Yu, Y. (2004). An Enhanced Technology Acceptance Model for Web-Based Learning. Journal of Information Systems Education, 15(4), 365-374.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis (6th ed.). Prentice Hall.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (1st ed.). Springer Nature.
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hammer, C. S. (2011). The Importance of Participant Demographics. American Journal of Speech-language Pathology, 20(4), 261.
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959-10966.
https://doi.org/10.1016/j.eswa.2012.03.028
Hayashi, A., Chen, C. C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139-154.
Hsieh, J. P., & Wang, W. (2007). Explaining employees’ extended use of complex information systems. European journal of information systems, 16(3), 216-227. https://doi.org/10.1057/palgrave.ejis.3000663
Hu, L., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Hussein, M. H., Ow, S. H., Ibrahim, I., & Mahmoud, M. A. (2021). Measuring instructors continued intention to reuse Google Classroom in Iraq: a mixed-method study during COVID-19. Interactive Technology and Smart Education, 18(3), 380-402. https://doi.org/10.1108/itse-06-2020-0095
iResearch. (2021). Chinese online education industry research report 2020, Shanghai, China.
https://report.iresearch.cn/report/202101/3724.shtml
Jiao, J., Zhou, X., & Chen, Z. (2020). Case analysis of the online instruction in the context of “classes suspended but learning continues” for plague prevention. China Educational Technology, 3(8), 106-113.
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.compedu.2018.01.003
Kanwal, F., & Rehman, M. (2017). Factors Affecting E-Learning Adoption in Developing Countries-Empirical Evidence from Pakistan’s Higher Education Sector. IEEE Access, 5, 10968-10978. https://doi.org/10.1109/access.2017.2714379
Kao, R., & Lin, C. (2018). The usage intention of e-learning for police education and training. Policing, 41(1), 98-112.
Lee, M.-C. (2010). Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
Lee, Y.-H., Hsiao, C., & Purnomo, S. H. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5), 562-579. https://doi.org/10.14742/ajet.381
Li, S., & Gao, J. (2020). The development process, connotation characteristics and quality monitoring of online teaching. Curriculum, teaching material and method, 40(11), 50-58.
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. Management Information Systems Quarterly, 31(4), 705-737. https://doi.org/10.2307/25148817
Lin, C. S., Wu, S. N., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42(5), 683-693.
https://doi.org/10.1016/j.im.2004.04.003
Liu, B. (2016). Online Teaching 4.0: The organic integration of online and offline. Construction and application of online education, 11, 67-69.
Malhotra, N. K., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of marketing research: an applied orientation (1st ed.). Pearson Education Australia.
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
Mouakket, S., & Bettayeb, A. M. (2015). Investigating the factors influencing continuance usage intention of Learning management systems by university instructors: The Blackboard system case. International Journal of Web Information Systems, 11(4), 491-509. https://doi.org/10.1108/ijwis-03-2015-0008
Oliver, R. P. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.1177/002224378001700405
Ong, C., & Lai, J. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816-829.
https://doi.org/10.1016/j.chb.2004.03.006
Ong, C., Lai, J., & Wang, Y. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41, 795-804.
https://doi.org/10.1016/j.im.2003.08.012
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50. https://doi.org/10.1177/002224298504900403
Pedroso, R. S., Zanetello, L. B., Guimarães, L. S. P., Pettenon, M. I. R., Gonçalves, V. M., Scherer, J. N., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Revista De Psiquiatria Clinica, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Petter, S., & McLean, E. R. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: an examination of IS success at the individual level. Information & Management, 46(3), 159-166. https://doi.org/10.1016/j.im.2008.12.006
Roca, J. a. M., Chiu, C., & Venegas-Martínez, F. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-computer Studies, 64(8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003
Rughoobur-Seetah, S., & Hosanoo, Z. A. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions. Quality Assurance in Education, 29(4), 422-444. https://doi.org/10.1108/qae-03-2021-0043
Salimon, M. G., Mokhtar, S. S. M., Aliyu, O. A., Yusr, M. M., & Perumal, S. (2021). Solving e-learning adoption intention puzzles among private universities in Nigeria: an empirical approach. Journal of Applied Research in Higher Education, 15(3), 613-631. https://doi.org/10.1108/JARHE-11-2020-0410.
Salkind, N. J. (2012). Exploring Research (8th ed.). Pearson Press.
Salloum, S. A., AlHamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model. IEEE Access, 7, 128445-128462. https://doi.org/10.1109/access.2019.2939467
Samarasinghe, S. M. (2012). E-Learning systems success in an organizational context. [Unpublished Doctoral Dissertation]. Massey University of New Zealand.
Segars, A. H., & Grover, V. (1993). Re-Examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis. Management Information Systems Quarterly, 17(4), 517-525. https://doi.org/10.2307/249590
Sun, S. (2016). Online Teaching 4.0: “Internet +” classroom teaching. The Chinese Journal of ICT in Education, 14, 17-20.
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(1), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229-243. https://doi.org/10.1080/13598660802232779
Zhang, C. (2020). Problems and countermeasures of online education reform in colleges and universities under the background of Internet +. Innovation and entrepreneurship theory research and practice, 1(19), 67-74.
Zhu, F. X., Wymer, W., & Chen, I. J. (2002). IT‐based services and service quality in consumer banking. International Journal of Service Industry Management, 13(1), 69-90. https://doi.org/10.1108/09564230210421164