Determinants of Satisfaction and Continuance Intention to Use Cloud-Based E-Learning Among Undergraduate Students in Ningxia Universities
DOI:
https://doi.org/10.14456/shserj.2023.52Keywords:
Cloud-Based E-Learning, Course Content Quality, Perceived Usefulness, Satisfaction, Continuance IntentionAbstract
Purpose: This study examines what factors affect the satisfaction and continuance intention of college students majoring in English translation and interpreting on cloud-based e-learning. The conceptual framework consists of 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: A quantitative research method was used to distribute questionnaires to three Ningxia universities and perform data analysis. Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) were applied to analyze the collected results and examine the pre-designed hypotheses' model fit, reliability, and validity. Results: It was found that satisfaction was the strongest predictor of continuance intention, followed by perceived usefulness. In addition to learning-technology fit as an antecedent, task-technology fit, interactivity, course content quality, course design quality, and organizational support showed significant and positive effects on satisfaction and perceived usefulness. Conclusion: Achieving and improving the satisfaction of students to use cloud-based e-learning is the priority for developers, administrators, and teachers. For organizations, adequate support for users and cloud-based e-learning is beneficial to enhance users' perceived usefulness. For developers, updating and ensuring a high degree of technical alignment of the system with the user's mission is an effective approach.
References
Adams, D., Chuah, K. M., Sumintono, B., & Mohamed, A. (2022). Students' readiness for e-learning during the COVID-19 pandemic in a South-East Asian university: a Rasch analysis. Asian Education and Development Studies, 11(2), 324-339. https://doi.org/10.1108/aeds-05-2020-0100
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
Alfraih, M. M., & Alanezi, F. S. (2016). Accounting students’ perceptions of effective faculty attributes. Journal of International Education in Business, 9(2), 123-142. https://doi.org/10.1108/jieb-04-2016-0004
Ali, S., Uppal, M. A., & Gulliver, S. R. (2018). A conceptual framework highlighting e-learning implementation barriers. Information Technology & People, 31(1), 156-180. https://doi.org/10.1108/itp-10-2016-0246
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
Alon, I., Farrell, M., & Li, S. (2020). Regime type and covid-19 response. FIIB Business Review, 9(3), 152-160.
https://doi.org/10.1177/2319714520928884
Amita, B. (2020). E-Learning experience of students in higher education institutions during the Covid-19 pandemic: a primary survey. In R. P. Singh, A. Singh & R. Kumar (Ed.), COVID-19 Pandemic: A Global Challenge (pp. 115-131). Aryan Publications.
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.
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
Chadda, I., & Kaur, H. (2021). COVID pandemic and virtual classes: a study of students from Punjab. Asian Association of Open Universities Journal, 16(2), 193-210. https://doi.org/10.1108/aaouj-02-2021-0017
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, E. W. L., Chu, S. K. W., & Ma, C. S. M. (2019). Students’ intentions to use PBworks: a factor-based PLS-SEM approach. Information and Learning, 120(7/8), 489-504.
https://doi.org/10.1108/ils-05-2018-0043
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. (2013). Roles of interactivity and usage experience in e-learning acceptance: a longitudinal study. International Journal of Web Information Systems, 10(1), 2-23.
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.
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. (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). Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation-confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202. https://doi.org/10.1108/jeim-12-2019-0401
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
Combe, C. (2005). Developing and implementing an online doctoral program. International Journal of Educational Management, 19(2), 118-127. https://doi.org/10.1108/09513540510582417
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
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.
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. Oxford University Press. Factor analysis. Psychometrika, 34(2), 183-202. 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.
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. Journal of 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 situation: 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
Hsiao, K.-L., & Chen, C.-C. (2015). How do we inspire children to learn with e-readers?. Library Hi Tech, 33(4), 584-596. https://doi.org/10.1108/lht-04-2015-0038
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
Isaías, P. (2018). Model for the enhancement of learning in higher education through the deployment of emerging technologies. Journal of Information, Communication and Ethics in Society, 16(4), 401-412.
https://doi.org/10.1108/jices-04-2018-0036
Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183-202. https://doi.org/10.1007/BF02289343.
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
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task-technology fit as users’ motivation to continue information system use. Computers in Human Behavior, 25(3), 778-784.
Lee, M. S., & An, H. (2018). A study of antecedents influencing eWOM for online lecture website: Personal interactivity as moderator. Online Information Review, 42(7), 1048-1064. https://doi.org/10.1108/oir-10-2017-0275
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.-C. (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, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers and Education, 54(2), 600-610.
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
Locke, E. A. (1976). The nature and causes of job satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297-1343). Rand McNally.
Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57(7), 738-756. https://doi.org/10.1108/et-02-2014-0014
McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers and Education, 52(2), 496-508. https://doi.org/10.1016/j.compedu.2008.10.002
McKeown, T., & Anderson, M. (2016). UTAUT: capturing differences in undergraduate versus postgraduate learning?. Education + Training, 58(9), 945-965. https://doi.org/10.1108/et-07-2015-0058
Min, Y., Huang, J., Varghese, M. M., & Jaruwanakul, T. (2022). Analysis of Factors Affecting Art Major Students’ Behavioral Intention of Online Education in Public Universities in Chengdu. AU-GSB E-JOURNAL, 15(2), 150-158.
https://doi.org/10.14456/augsbejr.2022.80
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
Oliver, R. L. (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
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
Rapanta, C., Botturi, L., Goodyear, P., Guardia, L., & Koole, M. (2020). Online university teaching during and after the Covid-19 crisis: refocusing teacher presence and learning activity. Postdigital Science and Education, 2(3), 923-945.
https://doi.org/10.1007/s42438-020-00155-y
Rigdon, E. E. (1998). Structural equation modeling (1st ed.). Lawrence Erlbaum Associates. https://doi.org/10.1002/9781118445112.stat06457
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
Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219-238.
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
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
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
Siritongthaworn, S., & Krairit, D. (2006). Satisfaction in e-learning: The context of supplementary instruction. Campus-Wide Information Systems, 23(2),76-91.
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
Taherdoost, H. (2016). Sampling methods in research methodology; How to choose a sampling technique for research. International Journal of Academic Research in Management, 5(2), 18-27. https://doi.org/10.2139/ssrn.3205035
Teo, A. C., Tan, G. W. H., Ooi, K. B., Hew, T. S., & Yew, K. T. (2015). The effects of convenience and speed in m-payment. Industrial Management and Data Systems, 115(2), 311-331. https://doi.org/10.1108/imds-08-2014-0231
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
Wang, Q., Zhu, Z., Chen, L., & Yan, H. (2009). E-learning in China. Campus-Wide Information Systems, 26(2), 77-81.
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
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
Zait, A., & Bertea, P. (2011). Methods for testing discriminant validity. Management & Marketing Journal, 9(2), 217-224.
