Factors Impacting on Satisfaction and Continuance Intention of English Literature Students on the Use of Cloud-based E-learning in Ningxia, China

Main Article Content

Nan Chen


Purpose: This study examines factors impacting the satisfaction and continuance intention of college students majoring in English literature on the use of cloud-based e-learning in Ningxia, China. The key variables involve task-technology fit, learning-technology fit, interactivity, course content quality, course design quality, organizational support, perceived usefulness, satisfaction and continuance intention. Research design, data, and methodology: This study was quantitatively conducted by sampling and distributing questionnaires to English literature students from three universities in Ningxia for quantitative research. The data results were analyzed, and the conceptual model was validated using CFA and SEM. Results: It was found that satisfaction was the strongest predictor of continuance intention, followed by perceived usefulness. All antecedents showed significant and positive effects on satisfaction and perceived usefulness. However, there was no correlation between perceived usefulness and satisfaction. Conclusion: Achieving and improving the satisfaction of students by paying to be fully aware of the interactivity, course content quality, and course content quality to use of cloud-based e-learning is the priority for developers, administrators, and teachers. Apart from this, the cloud-based e-learning adopted by the college needs to be responsive, novel, have enough interaction, and be relevant to their studies.


Download data is not yet available.

Article Details

How to Cite
Chen, N. (2024). Factors Impacting on Satisfaction and Continuance Intention of English Literature Students on the Use of Cloud-based E-learning in Ningxia, China. AU-GSB E-JOURNAL, 17(1), 11-23. https://doi.org/10.14456/augsbejr.2024.2
Author Biography

Nan Chen

Director of Student Office, The School of International Education, Ningxia University, China.


Alfadda, A., & Mahdi, H. S. (2021). Measuring Students’ Use of Zoom Application in Language Course Based on the Technology Acceptance Model (TAM). Journal of Psycholinguistic Research, 50(3), 883-900. https://doi.org/10.1007/s10936-020-09752-1

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. https://doi.org/10.5901/mjss.2015.v6n4s1p268

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

Aristovnik, A., Keržič, D., Tomaževič, N., & Lan, U. (2016). Demographic determinants of usefulness of e-learning tools among students of public administration. Interactive Technology and Smart, 13(4), 289-304. https://doi.org/10.1108/itse-09-2016-0033

Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit University Teknologi MARA.

Babbie, E. R. (1990). Survey research methods (1st ed). Cengage Learning Press.

Barhoumi, C. (2016). User acceptance of the e-information service as information resource: A new extension of the technology acceptance model, New Library World, 117(9/10), 626-643. https://doi.org/10.1108/nlw-06-2016-0045

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

Bijeesh, N. A. (2017). Advantages and Disadvantages of Distance Learning. https://www.indiaeducation.net/online-education/articles/advantages-and-disadvantages-of-distance-learning.html

Chang, C.-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

Chen, C. C., Lee, C. H., & Hsiao, K. L. (2017). 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). The Effects of Information Systems Quality on Nurses’ Acceptance of the Electronic Learning System. Journal of Nursing Research, 20(1), 19-31. https://doi.org/10.1097/jnr.0b013e31824777aa

Cheng, Y. M. (2013). 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. (2020). Students' satisfaction and continuance intention of the cloud-based e-learning system: roles of interactivity and course quality factors. Education + Training, 62(9), 1037-1059. https://doi.org/10.1108/et-10-2019-0245

Cheng, Y. M. (2021). Can tasks and learning be balanced? A dual-pathway model of cloud-based e-learning continuance intention and performance outcomes. Kybernetes, 51(1), 210-240. https://doi.org/10.1108/k-07-2020-0440

Choi, J. H., Kim, N. H., & An, D. (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering and System Safety, 133, 223- 226. https://doi.org/10.1016/j.ress.2014.09.014

Clark-Carter, D. (2009). Quantitative psychological research: the complete student's companion (3rd ed.). Psychology Press. https://doi.org/10.4324/9781315398143

Csikszentmihalyi, M. (1975). Beyond Boredom and Anxiety: Experiencing Flow in Work and Play (1st ed.). Jossey-Bass Publishers.

Dai, Y. (2017). Teaching Analysis for 3D Animation Course Based on The Flipped Classroom. Science & Technology Industry Parks, 22(1), 69-70.

Daultani, Y., Goswami, P. M., Kumar, A., & Pratap, S. (2020). Perceived outcomes of e-learning: identifying key attributes affecting user satisfaction in higher education institutes. Measuring Business Excellence, 25(2), 216-229. https://doi.org/10.1108/mbe-07-2020-0110

Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 357–369. https://doi.org/10.2307/249524

Dörnyei, Z. (2007). Research methods in applied linguistics (2nd ed.). Oxford University Press. https://doi.org/10.5054/tj.2010.215611

Gentile, T. A. R., Reina, R., De Nito, E., Bizjak, D., & Canonico, P. (2020). E-learning design and entrepreneurship in three European universities. International journal of Entrepreneurial Behavior & Research, 26(7), 1547-1566. https://doi.org/10.1108/ijebr-06-2019-0407

Gray, D. E. (2017). Doing Research in the Business World. SAGE Publications Ltd.

Gunesekera, A., Bao, Y. K., & Kibelloh, M. (2019). The role of usability on e-learning user interactions and satisfaction: a literature review. Journal of Systems and Information Technology, 21(3), 368-394. https://doi.org/10.1108/jsit-02-2019-0024

Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education + Training, 49(4), 336-337. https://doi.org/10.1108/et.2007.49.4.336.2

Hajli, M., Bugshan, H., Lin, X., & Featherman, M. (2013). From e-learning to social learning – a health care study. European Journal of Training and Development, 37(9), 851-863. https://doi.org/10.1108/ejtd-10-2012-0062

Hamdan, K. M., Al-Bashaireh, A. M., Zahran, Z., Al-Daghestani, A., AL-Habashneh, S., & Shaheen, A. M. (2021). University students’ interaction, Internet self-efficacy, self-regulation, and satisfaction with online education during pandemic crises of COVID-19 (SARS-CoV-2). International Journal of Educational Management, 35(3), 713-725. https://doi.org/10.1108/ijem-11-2020-0513

Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, L. T. M., Pham, A. T. V., & Dinh, H. V. (2021). Students’ adoption of e-learning in emergency: the case of a Vietnamese university during COVID-19. Interactive Technology and Smart Education, 18(2), 246-269. https://doi.org/10.1108/itse-08-2020-0164

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, 18(3), 380-402. https://doi.org/10.1108/itse-06-2020-0095

Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. https://doi.org/10.1007/BF02289343.

Kapo, A., Mujkic, A., Turulja, L., & Kovačević, J. (2020). Continuous e-learning at the workplace: the passport for the future of knowledge. Information Technology & People, 34(5), 1462-1489. https://doi.org/10.1108/itp-04-2020-0223

Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19. https://doi.org/10.1108/ijilt-05-2020-0090

Khan, H., Talib, F., & Faisal, M. N. (2015). An analysis of the barriers to the proliferation of m-commerce in Qatar: A relationship modeling approach. Journal of Systems and Information Technology, 17(1), 54–81. https://doi.org/10.1108/jsit-12-2014-0073

Kotler, P. (2000). Marketing in the twenty-first century: marketing management (10th ed.). Millenium.

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

Liu, M. (2009). The design of a web-based course for self-directed learning. Campus-Wide Information Systems, 26(2), 122-131. https://doi.org/10.1108/10650740910946846

Li, Y., & Kitcharoen, S. (2022). Determinants of Undergraduates’ Continuance Intention and Actual Behavior to Play Mobile Games in Chongqing, China. AU-GSB E-JOURNAL, 15(2), 206-214. https://doi.org/10.14456/augsbejr.2022.86

MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-226. https://doi.org/10.1146/annurev.psych.51.1.201

McConnell, D. (2018). E-learning in Chinese higher education: the view from inside. Higher Education, 75(6), 1031–1045. https://doi.org/10.1007/s10734-017-0183-4

Moon, S., Birchall, D., Williams, S., & Vrasidas, C. (2005). Developing design principles for an e-learning program for SME managers to support accelerated learning at the workplace. The Journal of Workplace Learning, 17(5/6), 370-384. https://doi.org/10.1108/13665620510606788

Mouakket, S., & Bettayeb, A. M. (2015). Investigating the factors influencing continuance usage intention of Learning management systems by university instructors. International Journal of Web Information Systems, 11(4), 491-509. https://doi.org/10.1108/ijwis-03-2015-0008

Murillo, A. P., & Jones, K. M. L. (2020). A “just-in-time” pragmatic approach to creating Quality Matters-informed online courses. Information and Learning Sciences, 121(5/6), 365-380. https://doi.org/10.1108/ils-04-2020-0087

Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081

Phutela, N., & Dwivedi, S. (2020). A qualitative study of students’ perspective on e-learning adoption in India. Journal of Applied Research in Higher Education, 12(4), 545-559. https://doi.org/10.1108/jarhe-02-2019-0041

Poondej, C., & Lerdpornkulrat, T. (2020). Gamification in e-learning: A Moodle implementation and its effect on student engagement and performance. Interactivity Technology and Smart Education, 17(1), 56-66. https://doi.org/10.1108/itse-06-2019-0030

Rodríguez Lera, F. J., Fernández González, D., Martín Rico, F., Guerrero-Higueras, Á. M., & Conde, M. Á. (2021). Measuring Students Acceptance and Usability of a Cloud Virtual Desktop Solution for a Programming Course. Applied Sciences, 11(15), 7157. https://doi.org/10.3390/app11157157

Rui-Hsin, K., & Lin, C. T. (2017). The usage intention of e-learning for police education and training. Policing: An International Journal, 41(1), 98-112. https://doi.org/10.1108/pijpsm-10-2016-0157

Salimon, M. G., Sanuri, S. M. M., Aliyu, O. A., Perumal, S., & Yusr, M. M. (2021). E-learning satisfaction and retention: a concurrent perspective of cognitive absorption, perceived social presence and technology acceptance model. Journal of Systems and Information Technology, 23(1), 109-129. https://doi.org/10.1108/jsit-02-2020-0029

Sánchez-López, L. (2013). Service-Learning Course Design for Languages for Specific Purposes Programs. American Association of Teachers of Spanish and Portuguese, 96(2), 383-396. https://doi.org/10.1353/hpn.2013.0059

Sarker, M. F. H., Mahmud, R. A., Islam, M. S., & Islam, M. K. (2019). Use of e-learning at higher educational institutions in Bangladesh: opportunities and challenges. Journal of Applied Research in Higher Education, 11(2), 210-223. https://doi.org/10.1108/jarhe-06-2018-0099

Sawang, S., Newton, C., & Jamieson, K. (2012). Increasing learners’ satisfaction/ intention to adopt more e-learning. Education + Training, 55(1), 83-105. https://doi.org/10.1108/00400911311295031

Scholtz, B., & Kapeso, M. (2014). An m-learning framework for ERP systems in higher education, Interactive Technology and Smart Education, 11(4), 287-301. https://doi.org/10.1108/itse-09-2014-0030

Shao, Z. (2017). 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

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286. https://doi.org/10.1016/j.jfoodeng.2005.02.010

Shea, T., & Parayitam, S. (2019). Antecedents of graduate student satisfaction through e-portfolio: content analysis. Education + Training, 61(9), 1045-1063. https://doi.org/10.1108/et-04-2019-0064

Shehzadi, S., Nisar, A. Q., Hussain, M. S., Hameed, W. S., & Chaudhry, N. I. (2020). The role of digital learning toward students’ satisfaction and university brand image at educational institutes of Pakistan: a post-effect of COVID-19. Digital learning and university brand image, 1(9), 1-10. https://doi.org/10.1108/aeds-04-2020-0063

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.

Singh, A., Sharma, S., & Paliwal, M. (2020). Adoption intention and effectiveness of digital collaboration platforms for online learning: the Indian students’ perspective, Interactive Technology and Smart Education, 18(4), 493-514. https://doi.org/10.1108/itse-05-2020-0070

Siqueira, F. V., Reichert, F. F., & Araujo, C. L. P. (2007). Gender differences in leisure-time physical activity. International Journal of Public Health, 53(8), 1-10. https://doi.org/10.1007/s00038-006-5062-1

Stoel, L., & Hye Lee, K. (2003). Modeling the effect of experience on student acceptance of Web‐based courseware. Internet Research, 13(5), 364-374. https://doi.org/10.1108/10662240310501649

Strauss, M., & Smith, G. (2009). Construct validity: Advances in theory and methodology. Annual review of clinical psychology, 5, 1-25. https://doi.org/10.1146/annurev.clinpsy.032408.153639

Teo, T. (2010). Modeling the determinants of pre-service teachers’ perceived usefulness of e-learning. Campus-Wide Information Systems, 28(2), 124-140. https://doi.org/10.1108/10650741111117824

Tu, Y. F., Hwang, G. J., Chen, J. C. C., & Lai, C. (2021). University students’ attitudes towards ubiquitous library-supported learning: an empirical investigation in the context of the Line@Library. The Electronic Library, 39(1), 186-207. https://doi.org/10.1108/el-03-2020-0076

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 76(6), 913-934. https://doi.org/10.1177/0013164413495237

Wu, J.-H., & Wang, Y.-M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002

Yang, R. (2020). China’s higher education during the COVID-19 pandemic: some preliminary observations. Higher Education Research and Development, 39(7), 1317–1321. https://doi.org/10.1080/07294360.2020.1824212

Zainab, B., Bhatti, M. A., Pangil, F. B., & Battour, M. M. (2015). E-training adoption in the Nigerian civil service. European Journal of Training and Development, 39(6), 538-564. https://doi.org/10.1108/ejtd-11-2014-0077