Drivers of Undergradute Students’ Perceived Usefulness and Satisfaction with Online Learning in Chengdu, China

Main Article Content

Haojing Du

Abstract

Purpose: The purpose of the study is to deeply explore the factors influencing perceived usefulness and satisfaction of undergraduates towards online learning experiences in China. In constructing the research framework, we selected seven latent variables: perceived ease of use, system quality, information quality, service quality, perceived usefulness, confirmation, and satisfaction. Research design, data, and methodology: This study applied quantitative approach, distributing questionnaire to 500 undergraduate students at three universities in Chengdu, Sichuan, China. Before the data collection, Item-Objective Congruence (IOC) and a pilot test of Cronbach's Alpha were adopted to test the content validity and reliability. In data analysis, the researcher used confirmatory factor analysis (CFA) and structural equation modeling (SEM) for statistical analysis to assess key indicators such as validity, reliability, model fits, and path coefficient. Results: The results show that perceived ease of use and service quality significantly influence perceived usefulness. The relationship among confirmation, perceived usefulness and satisfaction is supported. Additionally, perceived usefulness significantly influences satisfaction. Nevertheless, system quality and information quality have no significant influence on perceived usefulness. Conclusions: This research aims to gain a deeper understanding of the online learning experience to provide useful insights into the field of education.

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How to Cite
Du, H. (2024). Drivers of Undergradute Students’ Perceived Usefulness and Satisfaction with Online Learning in Chengdu, China. AU-GSB E-JOURNAL, 17(2), 123-132. https://doi.org/10.14456/augsbejr.2024.35
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Articles
Author Biography

Haojing Du

School of Literature and News Communication, Xihua University, China.

References

Aboelmaged, M. G. (2018). Predicting the success of Twitter in healthcare. Online Information Review, 42(6), 898–922.

Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405–420.

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Adoption of Management Information Systems in Context of Yemeni Organizations: A Structural Equation Modeling Approach. Journal of Digital Information Management, 13(6), 429-444.

Alon, I., & Canon, N. (2000). Internet-based experiential learning in international marketing: The case of Globalview.org. Online Information Review, 24(5), 349–356.

Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Universiti Teknologi Mara Kelantan.

Bashir, I., & Madhavaiah, C. (2014). Determinants of Young Consumers' Intention to Use Internet Banking Services in India. Vision: The Journal of Business Perspective, 18(3), 153-163. https://doi.org/10.1177/0972262914538369

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. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214.

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.

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.

Cheng, Y.-M. (2020). 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.

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.

Chiam, C. C., Woo, T. K., Chung, H. T., & Nair, P. R. K. (2017). The behavioural intention to use video lecture in an ODL institution. Asian Association of Open Universities Journal, 12(2), 206–217.

China Research Institute of Commerce and Industry. (2022, August 25). Summary of the latest policies of China's online education industry in 2022 (Table). https://www.askci.com/news/chanye/20220825/1752311968112.shtml

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), 216–227.

Elmorshidy, A. (2018). The impact of knowledge management systems on innovation. VINE Journal of Information and Knowledge Management Systems, 48(3), 388–403.

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

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Fry, K. (2001). E-learning markets and providers: Some issues and prospects. Education and Training, 43(4), 233–239.

Garcia, S. L., & Silva, C. M. (2017). Differences between perceived usefulness of social media and institutional channels by undergraduate students. Interactive Technology and Smart Education, 14(3), 196–215.

Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Researcher Methods for Business. Education + Training, 49(4), 336-337.

Hazari, S. I. (2004). Strategy for assessment of online course discussions. Journal of Information Systems Education, 15(4), 349–355.

Hong, S., Thong, J. Y. L., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile Internet. Decision Support Systems, 42(3), 1819–1834.

Ifinedo, P. (2017). Students’ perceived impact of learning and satisfaction with blogs. The International Journal of Information and Learning Technology, 34(4), 322–337.

Joo, S., & Choi, N. (2016). Understanding users’ continuance intention to use online library resources based on an extended expectation-confirmation model. The Electronic Library, 34(4), 554–571.

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.

Larson, P. D. (2002). Interactivity in an electronically delivered marketing course. Journal of Education for Business, 77(5), 265–269.

Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320–1329.

Lee, J. W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The Internet and Higher Education, 13(4), 277–283.

Lin, F., Fofanah, S. S., & Liang, D. (2011). Assessing citizen adoption of e-government initiatives in Gambia: A validation of the technology acceptance model in information systems success. Government Information Quarterly, 28(2), 271–279.

Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities: An empirical study. Internet Research, 17(2), 119–138.

Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers and Education, 48(2), 250–267.

Ortaçtepe, D. (2016). Using webcasts for student presentations: A case study. The International Journal of Information and Learning Technology, 33(1), 57–74.

Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285–1296.

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081

Rui, H. K., & Lin, C.-T. (2018). The usage intention of e-learning for police education and training. Policing: An International Journal, 41(1), 98–112.

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.

Shah, H. J., & Attiq, S. (2016). Impact of technology quality, perceived ease of use and perceived usefulness in the formation of consumer’s satisfaction in the context of e-learning. Abasyn Journal of Social Sciences, 9(1), 124–139.

Sharma, S., Pradhan, K., Satya, S., & Vasudevan, P. (2005). Potentiality of earthworms for waste management and in other uses- a review. Journal of American Science, 1(1), 4-16.

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 Science Publishers.

Soper, D. (2023). Calculator: A-priori Sample Size for Structural Equation Models. Daniel Soper. https://www.danielsoper.com/statcalc/calculator.aspx?id=89

Wang, J., & Xiao, J. J. (2009). Buying behavior, social support and credit card indebtedness of college students. International Journal of Consumer Studies, 33(1), 2–10.

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43(6), 728-739. http://dx.doi.org/10.1016/j.im.2006.05.002

Xu, F., Tian, M., Xu, G., Ayala, B. R., & Shen, W. (2017). Understanding Chinese users’ switching behaviour of cloud storage services. The Electronic Library, 35(2), 214–232.