Factors Impacting on Satisfaction and Behavioral Intention of Social Science Majors Students Toward E-learning: A Case Study of a public university in Sichuan, China
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Purpose: This research aims to examine the factors impacting social science majors’ students’ satisfaction and behavioral Intention to use electronic learning (E-learning) in a public university in Sichuan, China. The conceptual framework identifies the causal relationship between system quality, satisfaction, performance expectancy, effort expectancy, social influence, attitude, and behavioral intention. Research design, data, and methodology: Sample data was collected using the quantitative method and a questionnaire (N=500) as a tool. Item-Objective Congruence and pilot tests were adopted to test the content validity and reliability of the questionnaire before distribution. Data was analyzed by utilizing Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate the model’s goodness of fit and confirm the causal relationship among variables for hypothesis testing. Results: System quality, satisfaction, performance expectancy, effort expectancy, social influence, and attitude significantly impact behavioral intention. Furthermore, performance expectancy has the strongest impact on the behavioral intention of E-learning among social science majors’ students. Conclusions: The creator of the course curriculum, the instructors, and the administration should guarantee the system’s high quality. It is recommended that professors and university administration employ the active learning technique in online lectures to ensure maximum student participation and arouse their interest.
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References
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action-Control: From Cognition to Behavior (pp. 11-39). Springer-Verlag.
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1007/978-3-642-69746-3_2
Alharbi, S., & Drew, S. (2014). Mobile Learning-System Usage: Scale Development and Empirical Tests. International Journal of Advanced Research in Artificial Intelligence, 3(11), 31-47. https://doi.org/10.14569/ijarai.2014.031105
Alksasbeh, M., Abuhelaleh, M., & Almaiah, M. (2019). Towards a model of quality features for mobile social networks apps in learning environments: An extended information system success model. International Journal of Interactive Mobile Technologies (iJIM), 13(5), 1-19. https://doi.org/10.3991/ijim.v13i05.9791
Allen, M., Frame, D. J., Huntingford, C., & Jones, C. (2009). Warming Caused by Cumulative Carbon Emissions Towards the Trillionth, Tonne. Nature, 458(7242), 1163-6.
Al-Mamary, Y. H., Shamsuddin, A., Hamid, N. A., & Al-Maamari, M. H. (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.
Awang, Z. (2012). Structural equation modeling using AMOS graphic (5th ed.). Penerbit Universiti Teknologi MARA.
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
Beran, T. N., & Violato, C. (2010). Structural equation modeling in medical research: a primer. BMC Research Notes, 3(1). https://doi.org/10.1186/1756-0500-3-267
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921
Boateng, R., Mbrokoh, A. S., Boateng, L., Senyo, P. K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. International Journal of Information and Learning Technology, 33(4), 248-262. https://doi.org/10.1108/ijilt-02-2016-0008
Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191. https://doi.org/10.1108/jrit-03-2017-0004
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: an application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
Chiu, Y.-B., Lin, C.-P., & Tang, L.-L. (2005). Gender differs: Assessing a model of online purchase intentions in e-tail service. International Journal of Service Industry Management, 16(5), 416-435.
Cidral, W., Aparicio, M., & Oliveira, T. (2020). Students’ long-term orientation role in e-learning success: A Brazilian study. Heliyon, 6(12), e05735. https://doi.org/10.1016/j.heliyon.2020.e05735
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information system success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.
Elliott, K. M., & Shin, D. (2002). Student Satisfaction: an alternative approach to assessing this important concept. Journal of Higher Education Policy and Management, 24(2), 197-209. https://doi.org/10.1080/1360080022000013518
El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743–763. https://doi.org/10.1007/s11423-016-9508-8
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/10.2307/3151312
Gonzalez, J., Boeck, P., & Tuerlinckx, F. (2008). A double structure structural equation model for three-mode data. Psychological Methods, 13(4), 337-353. https://doi.org/10.1037/a0013269
Gupta, N., & Sharma, V. (2016). Exploring employee engagement—A way to better business performance. Global Business Review, 17(3), 45-63. https://doi.org/10.1177/0972150916631082
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice-Hall.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New Challenges to International Marketing (Advances in International Marketing, Vol. 20, pp. 277-319). Emerald Group Publishing Limited.
Ho, L. A., Kuo, T. H., & Lin, B. (2010). Influence of online learning skills in cyberspace. Internet Research, 20(1), 55-71. https://doi.org/10.1108/10662241011020833
Hsiao, C. H., & Tang, K. Y. (2014). Explaining undergraduates’ behavior intention of e-textbook adoption: Empirical assessment of five theoretical models. Library Hi Tech, 32(1), 139-163. https://doi.org/10.1108/LHT-09-2013-0126
Huang, J., & Duangekanong, S. (2022). Factors Impacting the Usage Intention of Learning Management System in Higher Education. AU-GSB E-JOURNAL, 15(1), 41-51. https://doi.org/10.14456/augsbejr.2022.59
Ibukun, E., Okuboyejo, S., & Kelechi, A. (2016). The adoption of E-tourism: an empirical investigation. Asian Journal of Information Technology, 15(18), 3422-3429.
Jamal, A., & Naser, K. (2003). Factors Influencing Customer Satisfaction in the Retail Banking Sector in Pakistan. International Journal of Commerce and Management, 13(2), 29-53. https://doi.org/10.1108/eb047465
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
Kumar Basak, S., Wotto, M., & Belanger, P. (2018). E-learning, M-learning, and D-learning: conceptual definition. Journal indexing and metrics and comparative analysis. E-learning and Digital Media, 15(4), 191-216. https://doi.org/10.1177/2042753018785180
Kwok, S. H., & Gao, S. (2005). Attitude towards knowledge sharing behaviour. Journal of Computer Information Systems, 46(2), 45-51.
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). Technology acceptance model and the world wide web. Decision Support Systems, 29(3), 269-282. https://doi.org/10.1016/S0167-9236(00)00076-2
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. https://doi.org/10.1016/j.compedu.2009.06.014
Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: supporting employees’ intentions to use e-learning systems. Journal of Educational Technology and Society, 14(4), 124-137.
Leong, P. (2011). Role of social presence and cognitive absorption in online learning environments. Distance Education, Taylor & Francis, 32(1), 5-28. https://doi.org/10.1080/01587919.2011.565495
Liao, C., Palvia, P., & Chen, J. L. (2009). Information technology adoption behavior life cycle: toward a technology continuance theory (TCT). International Journal of Information Management, 29(4), 309-320. https://doi.org/10.1016/j.ijinfomgt.2009.03.004
Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 105-117.
Miller, S. D., Duncan, B. L., & Brown, J. (2003). The Outcome Rating Scale: A Preliminary Study of the Reliability, Validity, and Feasibility of a Brief Visual Analog Measure. Journal of brief Therapy, 2(2), 91-100.
Ngai, E. W., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of Web CT using TAM. Computers & Education, 48(2), 250-267. https://doi.org/10.1016/j.compedu.2004.11.007
Patricia, A. H. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011.
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
Reynolds, P. (2011). UDENTE (universal dental E-learning) a golden opportunity for dental education. Bulletin of the International Group for Scientific Research in Stomatology and Odontology, 50(3), 11-19.
Rosenberg, M. (2001). e-learning: Strategies for delivering knowledge in the digital age (1st ed.). McGraw Hill.
Rudhumbu, N. (2020). Antecedents of university lecturers’ intentions to adopt information and communication technology in Zimbabwe. Education and Information Technologies, 25(6), 5117-5132. https://doi.org/10.1007/s10639-020-10205-4
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, 24(1), 509-530.
Sekaran, U. (1992). Research Methods for Business – A skill building approach (2nd ed). John Wiley and Sons, Inc.
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
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.
Sihar, S., Hj Ab Aziz, S., & Sulaiman, Z. A. (2011). Design and development of semiconductor courseware for undergraduate students. Journal of Applied Sciences, 11(5), 883-887. https://doi.org/10.3923/jas.2011.883.887
Soper, D. S. (2020). A-Priori Sample Size Calculator for Structural Equation Models [Software]. http://wwwdanielsopercom/statcalc
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers and Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007
Tan, P. J. B. (2013). Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan. SAGE Open, 3(4), 215824401350383. https://doi.org/10.1177/2158244013503837
Thongsri, N., Shen, L., Bao, Y., & Alharbi, I. M. (2018). Integrating UTAUT and UGT to explain behavioural intention to use M-learning. Journal of Systems and Information Technology, 20(3), 278-297. https://doi.org/10.1108/jsit-11-2017-0107
Upadhyay, N., Upadhyay, S., Abed, S. S., & Dwivedi, Y. K. (2022). Consumer adoption of mobile payment services during COVID-19: extending meta- UTAUT with perceived severity and self-efficacy. International Journal of Bank Marketing, 40(5), 960-991. https://doi.org/10.1108/ijbm-06-2021-0262
Van Hoa, T. T., Huyen, P. T., & Hoa, N. Q. (2020). Covid-19 pandemic: seeking opportunities in challenges for Vietnamese universities in a new context. Journal of Economics and Development, 274, 64-74.
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational behavior and human decision processes, 83(1), 33-60. https://doi.org/10.1006/obhd.2000.2896
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Wang, H. Y., & Wang, S. H. (2010). User acceptance of mobile internet based on the unified theory of acceptance and use of technology: investigating the determinants and gender differences. Social Behavior and Personality, 38(3), 415-426. https://doi.org/10.2224/sbp.2010.38.3.415
Wang, M. H. (2016). Factors Influencing Usage of E-learning Systems in Taiwan’s Public Sector: Applying the UTAUT Model. Advances in Management & Applied Economics, 6(6), 63-82.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002
Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning. Asia Pacific Journal of Teacher Education, 36(3), 229-243. https://doi.org/10.1080/13598660802232779