An Empirical Investigation of Elementary Art Teachers’ Satisfaction and Continuance Intention to Use E-Learning Systems in Chongqing, China
DOI:
https://doi.org/10.14456/shserj.2024.24Keywords:
E-learning, Primary Schools, Service Quality, Satisfaction, Continuance IntentionAbstract
Purpose: This essay aims to assess critical factors that significantly impact the satisfaction and continuance intention of art teachers from primary schools in Chongqing Province of China for online education. In the research framework, it presents the causal relationship between engagement, course structure, system quality, information quality, service quality, perceived usefulness, satisfaction, and continuance intention. Research design, data, and methodology: The researcher applied a quantitative method to distribute the quantitative questionnaire to 500 elementary art teachers at 20 schools. The sampling strategies are used to collect the data, including judgmental, quota and convenience sampling. Before the data collection, the expert rating of the item's index–objective congruence (IOC) and pilot test for 50 respondents have been tested. This study employed a structural equation model (SEM) and confirmatory factor analysis (CFA). Result: Six out of eight hypotheses were supported. Perceived usefulness and satisfaction significantly impact continuance intention. There are non-supported relationships between course structure, sytem quality and satisfaction. Conclusions: Administrators need to pay close attention to the elements that greatly influence satisfaction in order for primary school art teachers to acknowledge and recognize the effectiveness of online education. They should also consider the research's findings when adjusting or reforming correlated instruction.
References
Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information & management, 44(3), 263-275. https://doi.org/10.1016/j.im.2006.12.008
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in human behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of management education, 24(1), 32-54. https://doi.org/10.1177/105256290002400104
Baroudi, J. J., & Orlikowski, W. J. (1988). A short-form measure of user information satisfaction: a psychometric evaluation and notes on use. Journal of Management Information Systems, 4(4), 44-59. https://doi.org/10.1080/07421222.1988.11517807
Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107(2), 238-246. http://dx.doi.org/10.1037/0033-2909.107.2.238
Bharati, P., & Chaudhury, A. (2004). An empirical investigation of decision-making satisfaction in web-based decision support systems. Decision support systems, 37(2), 187-197. https://doi.org/10.1016/s0167-9236(03)00006-x
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS quarterly, 28(2), 229-254. https://doi.org/10.2307/25148634
Birch, L. L., Fisher, J. O., Grimm-Thomas, K., Markey, C. N., & Sawyer, R. (2001). Confirmatory factor analysis of the Child Feeding Questionnaire: a measure of parental attitudes, beliefs and practices about child feeding and obesity proneness. National Library of Medicine, 36(3), 201-210.
Bitner, M. J. (2000). The Servicescape in Handbook of Services Marketing and Management (1st ed.). Sage.
Bitzer, P., & Janson, A. (2014). Towards a holistic understanding of technology mediated learning services–a state-of-the-art analysis. European Conference on Information Systems (ECIS), Tel Aviv, Israel, 1-19.
Budu, K. W. A., Yinping, M., & Mireku, K. K. (2018). Investigating the effect of behavioral intention on e-learning systems usage: Empirical study on tertiary education institutions in Ghana. Mediterranean Journal of Social Sciences, 9(3), 201-216. https://doi.org/10.2478/mjss-2018-0062
Chen, C. F., & Chen, F. S. (2010). Experience quality, perceived value, satisfaction, and behavioral intentions for heritage tourists. Tourism management, 31(1), 29-35. https://doi.org/10.1016/j.tourman.2009.02.008
Chen, S.-C., Chen, H.-H., & Chen, M.-F. (2009). Determinants of satisfaction and continuance intention towards self-service technologies. Industrial Management & Data Systems, 109(9), 1248-1263. https://doi.org/10.1108/02635570911002306
Cheung, G. W., & Wang, C. (2017). Current approaches for assessing convergent and discriminant validity with SEM: Issues and solutions. In Academy of management proceedings, Briarcliff Manor, NY 10510: Academy of Management, 17(1), 12706. https://doi.org/10.5465/ambpp.2017.12706abstract
Clark-Carter, D. (1997). Doing quantitative psychological research: From design to report (3rd ed.). Psychology Press.
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation, and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning, 11(2), 201-214.
DeCoster, J. (n.d.). Overview of factor analysis. http://www.stat-help.com/notes.html
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Journal of Management Information Systems, 3(4), 60-95. https://doi.org/10.2753/mis0742-1222290401
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.
Dennen, V. P., Darabi, A. A., & Smith, L. J. (2007). Instructor-learner interaction in online courses: The relative perceived importance of instructor actions on performance and satisfaction. Distance Education, 28(1), 65-79.
https://doi.org/10.1080/01587910701305319
Doll, W. J., & Torkzadeh, G. (1998). Developing a multidimensional measure of system-use in an organizational context. Information & Management, 33(4), 171-185. https://doi.org/10.1016/s0378-7206(98)00028-7
Donthu, N., & Yoo, B. (1998). Cultural influences on service quality expectations. Journal of service research, 1(2), 178-186. https://doi.org/10.1177/109467059800100207
Dovaliene, A., Masiulyte, A., & Piligrimiene, Z. (2015). The relations between customer engagement, perceived value, and satisfaction: the case of mobile applications. Procedia-Social and Behavioral Sciences, 213(1), 659-664.
Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students' perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215-235. https://doi.org/10.1111/j.1540-4609.2006.00114.x
Freeman, S., Haak, D., & Wenderoth, M. P. (2011). Increased course structure improves performance in introductory biology. CBE—Life Sciences Education, 10(2), 175-186. https://doi.org/10.1187/cbe.10-08-0105
Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study–A case of China. Computers in Human Behavior, 53, 249-262.
https://doi.org/10.1016/j.chb.2015.07.014
Gray, J. A., & Diloreto, M. (2016). The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation, 11(1), 1-20.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results, and higher acceptance. Long range planning, 46(1-2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001
Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Harsasi, M., & Sutawijaya, A. (2018). Determinants of student satisfaction in online tutorial: A study of a distance education institution. Turkish Online Journal of Distance Education, 19(1), 89-99. https://doi.org/10.17718/tojde.382732
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
He, K. L. (2002). E-learning and the deepening reform of university teaching. E-Education in China, 1(02), 8-12.
Hok, T., Daengdej, J., & Vongurai, R. (2021). Determinants of Student Satisfaction on Continuing Education Intention: A Case Study of Private University in Cambodia. AU-GSB E-JOURNAL, 14(2), 40-50. https://doi.org/10.14456/augsbejr.2021.13
Hong, J.-C., Lin, P.-H., & Hsieh, P.-C. (2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior, 67, 264-272. https://doi.org/10.1016/j.chb.2016.11.001
Hoyle, R. H. (2000). Confirmatory Factor Analysis. Handbook of Applied Multivariate Statistics and Mathematical Modeling, 465-497. https://doi.org/10.1016/b978-012691360-6/50017-3
Hu, S., & Kuh, G. D. (2002). Being (dis)engaged in educationally purposeful activities: The influences of student and institutional characteristics. Research in Higher Education, 43(5), 555-575.
iResearch. (2021, June 6). 2020Q1 & 2020Q2e China Online Education Market Data Release Report. iResearch. http://report.iresearch.cn/report_pdf.aspx?id=3599
Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. The Guilford Press, 8(12), 1-10.
Koivumäki, T., Ristola, A., & Kesti, M. (2008). The effects of information quality of mobile information services on user satisfaction and service acceptance–empirical evidence from Finland. Behaviour & Information Technology, 27(5), 375-385. https://doi.org/10.1080/01449290601177003
Kuh, G. D. (2001). The National Survey of Student Engagement: Conceptual framework and overview of psychometric properties. Framework & Psychometric Properties, 1(2), 1-26.
Kuo, Y.-F., Wu, C.-M., & Deng, W.-J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior, 25(4), 887-896. https://doi.org/10.1016/j.chb.2009.03.003
Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & management, 42(8), 1095-1104. https://doi.org/10.1016/j.im.2003.10.007
Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26-34. https://doi.org/10.1016/j.iheduc.2014.09.005
Mandernach, B. J., Donnelli-Sallee, E., & Dailey-Hebert, A. (2011). Assessing course student engagement. Promoting student engagement, 1, 277-281.
Marsh, H. W., Byrne, B., & Shavelson, R. J. (1988). A multifaceted academic self-concept. Its’ hierarchical structure and its’ relation to academic achievement. Journal of Educational Psychology, 80, 366380. http://dx.doi.org/10.1037/0022-0663.80.3.366
McGill, T., Hobbs, V., & Klobas, J. (2003). User developed applications and information systems success: A test of DeLone and McLean's model. Information Resources Management Journal (IRMJ), 16(1), 24-45. https://doi.org/10.4018/irmj.2003010103
Moore, M. G. (1991). Editorial: Distance education theory. The American Journal of Distance Education, 5(3), 1-6.
https://doi.org/10.1080/08923649109526758
Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior, 53, 102-110. https://doi.org/10.1016/j.chb.2015.06.045
Muirhead, B. (2004). Encouraging interaction in online classes. International Journal of Instructional Technology and Distance Learning, 1(6), 45-50.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Olorunniwo, F., Hsu, M. K., & Udo, G. J. (2006). Service quality, customer satisfaction, and behavioral intentions in the service factory. Journal of services marketing, 20(1), 59-72. https://doi.org/10.1108/08876040610646581
Ostroff, J., Shelley, D., Chichester, L. A., King, J. C., Li, Y., Schofield, E., & Kenney, J. (2022). Study protocol of a multiphase optimization strategy trial for delivery of smoking cessation treatment in lung cancer screening settings. National library of medicine, 23(1), 664. https://doi.org/10.21203/rs.3.rs-1044243/v1
Ozkan, S., Koseler, R., & Baykal, N. (2009). Evaluating learning management systems: Adoption of hexagonal e‐learning assessment model in higher education. Transforming government: people, process, and policy, 3(2), 111-130. https://doi.org/10.1108/17506160910960522
Pham, L., Limbu, Y. B., Bui, T. K., Nguyen, H. T., & Pham, H. T. (2019). Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam. International Journal of Educational Technology in Higher Education, 16(1), 1-26. https://doi.org/10.1186/s41239-019-0136-3
Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information & management, 39(6), 457-465. https://doi.org/10.1016/s0378-7206(01)00112-4
Saadé, R. G. (2007). Dimensions of Perceived Usefulness: Toward Enhanced Assessment. Decision Sciences Journal of Innovative Education, 5(2), 289-310.
Saeed, K. A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & management, 45(6), 376-386. https://doi.org/10.1016/j.im.2008.06.002
Sedera, D., Gable, G., & Chan, T. (2004). A factor and structural equation analysis of the enterprise systems success measurement model. In Proceedings of the 10th Americas conference on information systems, 676-682.
Shao, Z., Guo, Y., & Ge, C. (2019). Impact of Perceived Value on Customer Satisfaction and Continuance Intention of Bicycle Sharing Service. Hawaii International Conference on System Sciences, 1(2), 1-10. https://doi.org/10.24251/hicss.2019.114
Shin, N., & Chan, J. K. (2004). Direct and indirect effects of online learning on distance education. British Journal of Educational Technology, 35(3), 275-288. https://doi.org/10.1111/j.0007-1013.2004.00389.x
Soper, D. S. (n.d.). A-Priori Sample Size Calculator for Structural Equation Models [Software]. http://wwwdanielsopercom/statcalc
Sørebø, Ø., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to continue to use e-learning technology. Computers & Education, 53(4), 1177-1187. https://doi.org/10.1016/j.compedu.2009.06.001
Srinivasan, A. (1985). Alternative measures of system effectiveness: associations and implications. MIS quarterly, 9(3), 243-253. https://doi.org/10.2307/248951
Sureshchandar, G. S., & Leisten, R. (2006). Software metrics for enhanced business excellence: An investigation of research issues from a macro perspective. Total Quality Management and Business Excellence, 17(5), 609-622. https://doi.org/10.1080/14783360600588174
Swan, K. (2001). Virtual interaction: Design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance education, 22(2), 306-331. https://doi.org/10.1080/0158791010220208
Taherdoost, H. (2017). Determining sample size; how to calculate survey sample size. International Journal of Economics and Management Systems, 2, 1-10.
Tarn, J. L. M. (1999). The Effects of Service Quality, Perceived Value and Customer Satisfaction on Behavioral Intentions. Journal of Hospitality & Leisure Marketing, 6(4), 31-43. https://doi.org/10.1300/j150v06n04_04
Tsai, P. C. F., Yen, Y. F., Huang, L. C., & Huang, C. (2007). A study on motivating employees’ learning commitment in the post-downsizing era: Job satisfaction perspective. Journal of world business, 42(2), 157-169. https://doi.org/10.1016/j.jwb.2007.02.002
Ullman, J. B., & Bentler, P. M. (2012). Structural equation modeling. Handbook of Psychology, 2, 1-10. https://doi.org/10.1002/0471264385.wei0224
Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS quarterly, 25(1), 71-102. https://doi.org/10.2307/3250959
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), 425-478. https://doi.org/10.2307/30036540
Wang, Y. S., & Liao, Y. W. (2008). Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success. Government information quarterly, 25(4), 717-733. https://doi.org/10.1016/j.giq.2007.06.002
Wang, Y., Wen, Z. L., Li, W., & Fang, J. (2022). Research and model development of domestic structural equation modeling methods in the new century 20 years. Advances in Psychological Science, 30(8), 1715-1733.
Yu, H. S., Zhang, J. J., Kim, D. H., Chen, K. K., Henderson, C., Min, S. D., & Huang, H. (2014). Service quality, perceived value, customer satisfaction, and behavioral intention among fitness center members aged 60 years and over. Social Behavior and Personality: an international journal, 42(5), 757-767. https://doi.org/10.2224/sbp.2014.42.5.757