Influencers of Senior High Sciences Students’ Satisfaction and Behavioral Intention to Use Online Learning in Panzhihua, China

Main Article Content

Luqing Yang

Abstract

Purpose: The purpose of this article is to investigate the indicators that influence satisfaction and behavioral intention with online education in Panzhihua, China, and the investigation was conducted using a quantitative survey assessment strategy. The conceptual framework incorporated system quality, information quality, service quality, effort expectancy, social influence, satisfaction, and behavioral intention. Research design, data, and methodology: This study applied quantitative method to collect the data. The researcher distributed the questionnaire to the 461 sciences students. Item-Objective Congruence (IOC) and Cronbach's Alpha gauge the validity and reliability. IOC reveals that each scale item achieved a rating of 0.6 or greater. Cronbach alpha coefficient reliability affirms values equal to or surpassing 0.7. The sampling methods encompass judgmental, stratified random, and convenience sampling. Data analysis involved the utilization of Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results: All six hypotheses were determined to be established. System quality, information quality, service quality significantly influences on satisfaction. Furthermore, behavioral intention is significantly influenced by satisfaction, effort expectancy, and social influence. Conclusions: Therefore, if students are satisfied with their online learning experience, their behavioral sense for employment the online learning through the Huidao education system will be enhanced.

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Yang, L. (2024). Influencers of Senior High Sciences Students’ Satisfaction and Behavioral Intention to Use Online Learning in Panzhihua, China. AU-GSB E-JOURNAL, 17(3), 39-48. https://doi.org/10.14456/augsbejr.2024.47
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Articles
Author Biography

Luqing Yang

Panzhihua No.7 Senior High School.

References

Ahmad, M. D., & Tarmudi, S. M. (2012). Generational differences in satisfaction with e-learning among higher learning institution staff. Procedia Social and Behavioral Sciences, 67, 304-311. https://doi.org/10.1016/j.sbspro.2012.11.333

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

Almarashdeh, I. (2016). Sharing instructors experience of learning management system: A technology perspective of user satisfaction in distance learning course. Computers in Human Behavior, 63, 249-255. https://doi.org/10.1016/j.chb.2016.05.013

Alzahrani, A. I., Mahmud, I., Ramayah, T., & Alalwan, N. (2017). Modelling digital library success using the DeLone and McLean information system success model. Journal of Librarianship and Information Science, 51(2), 291-306. https://doi.org/10.1177/0961000617726123

Aparicio, M., Bacao, F., & Oliveira, T. (2014). Trends in the e-learning ecosystem: A Bibliometric study [Paper Presentation]. Proceedings of 20th American Conference on Information System. http://aisel.aisnet.org/amcis2014/Posters/ISEducation/7

Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the Path to E-learning Success. Computers in Human Behavior, 66, 388-399. https://doi.org/10.1016/j.chb.2016.10.009

Attuquayefio, S. N., & Addo, H. (2014). Using UTAUT Model to Analysis Students’ ICT Adoption. International Journal of Education and Development Using Information and Communication Technology, 10(3), 75-86.

Awang, Z. (2012). Structural equation modelling using AMOS graphic (5th ed.). Penerbit Universiti Teknologi MARA.

Babin, B., & Babin, L. (2001). Seeking something different? A model of schema typicality, consumer affect, purchase intentions and perceived shopping value. Journal of Business Research, 54(2), 89-96. https://doi.org/10.1016/s0148-2963(99)00095-8

Bardakcı, S. (2019). Exploring High School Students’ Educational Use of YouTube. International Review of Research in Open and Distributed Learning, 20(2), 260-278. https://doi.org/10.19173/irrodl.v20i2.4074

Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238-246. http://dx.doi.org/10.1037/0033-2909.107.2.238

Chaka, J. G., & Govender, I. (2017). Students’ Perceptions and Readiness Towards Mobile Learning in Colleges of Education: A Nigerian Perspective. South African Journal of Education, 37(1), 1-12. https://doi.org/10.15700/saje.v37n1a1282

Chang, C. C. (2012). 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

Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390. https://doi.org/10.1108/10662241211235699

Clemes, M. D., Gan, C., & Kao, T. H. (2008). University Student Satisfaction: An Empirical Analysis. Journal of Marketing for Higher Education, 17(2), 292-325. https://doi.org/10.1080/08841240801912831

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, And User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

Davis, F. D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts. International Journal of Man-Machine Studies, 38 (3), 475-487. https://doi.org/10.1006/imms.1993.1022

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/ Spring, 19(4), 9-30.

Eom, S., Ashill, N. J., Arbaugh, J. B., & Stapleton, J. L. (2012). The Role of Information Technology in E-learning Systems Success. Human Systems Management, 31(3), 147-163. https://doi.org/10.3233/hsm-2012-0767

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 24(4), 337-346. https://doi.org/10.2307/3151312

Grönroos, C. (1984). A service quality model and its marketing implications. European Journal of Marketing, 18(4), 36-44. https://doi.org/10.1108/eum0000000004784

Hair, J. F., Anderson, R., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice-Hall.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice-Hall.

Hao, S., Dennen, P., & Mei, L. (2017). Influential Factors for Mobile Learning Acceptance Among Chinese Users. Educational Technology Research and Development, 65(1), 101-123. https://doi.org/10.1007/s11423-016-9465-2

Haslina, H., Hashim, I. C., Abdullah, S., Isa, F. M., & Talib, N. (2014 October 7-8). Geographical Information Systems (GIS) Approach for Mapping the Aboriginal Children Malnutrition Growth: A Case in Kemar, Perak [Paper Presentation]. Proceedings of Postgraduate Conference on Global Green Issues (Go Green), UiTM (Perak), Malaysia.

Jairak, K., Praneetpolgrang, P., & Mekhabunchakij, K. (2009). An Acceptance of Mobile Learning for Higher Education Students in Thailand. International Journal of The Computer, the Internet and Management, 17(3), 361-368.

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. https://doi.org/10.1108/el-02-2015-0033

Joo, Y. J., Joung, S., Shin, E. K., Lim, E., & Choi, M. (2014). Factors Influencing Actual Use of Mobile Learning Connected with E-Learning. In D. C. Wyld & J. Zizka (Eds), Computer Science & Information Technology (pp. 169-176). Third International Conference on Advanced Information Technologies & Applications (ICAITA-2014). https://doi.org/10.5121/csit.2014.41116

Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language (1st ed.). Lawrence Erlbaum Associates, Inc.

Kong, S. C., Chan, T. W., Griffin, P., Hoppe, U., Huang, R., Kinshuk Looi, C. K., Norris, C., Nussbaum, M., Sharples, M., So, W. M. W., Soloway, E., & Yu, S. (2014). E-learning in school education in the coming 10 years for developing 21st century skills: Critical research issues and policy implications. Journal of Educational Technology & Society, 17(1), 70-78.

Lin, H.-F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119-138. https://doi.org/10.1108/10662240710736997

Liu, X., Chen, S., & Ding, L. (2018). Research on Intelligent Classroom Teaching based on Huidao Platform – The Empirical Research in a Middle School in Luzhou of Sichuan. Chinese Journal of ICT in Education, 1(2) 51-54.

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

Machleit, K., & Mantel, S. (2001). Emotional response and shopping satisfaction: moderating effects of shopper attributions. Journal of Business Research, 54(2), 97-106. https://doi.org/10.1016/s0148-2963(99)00093-4

Marchewka, J. T., & Kostiwa, K. (2007). An Application of the UTAUT Model for Understanding Student Perceptions Using Course Management Software. Communications of the IIMA, 7(2), 93-104. https://doi.org/10.58729/1941-6687.1038

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based practices in online learning: A Meta-analysis and review of online learning studies. US Department of Education. www.ed.gov/about/offices/list/opepd/ppss/reports.html

Munadi, M., Annur, F., & Saputra, Y. (2022). Student Satisfaction in Online Learning of Islamic Higher Education in Indonesia during the Second Wave of COVID-19 Pandemic. Journal of Education and e-Learning Research, 9(2), 87-94. https://doi.org/10.20448/jeelr.v9i2.3952

Nagy, J. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Mode. International Review of Research in Open and Distributed Learning, 19(1), 160-185. https://doi.org/10.19173/irrodl.v19i1.2886

Napitupulu, T. A., & Patria, S. H. J. (2013). Factors that determine electronic medical records users’ satisfaction: a case of Indonesia. Journal of Theoretical and Applied Information Technology, 58(3), 499-505.

OECD. (2012). Education at a glance 2012: Highlights. Paris, France: OECD Publishing.

Ofori, K. S., Boakye, K., & Narteh, B. (2018). Factors influencing consumer loyalty towards 3G mobile data service providers: evidence from Ghana. Total Quality Management & Business Excellence, 29(5-6), 580-598. https://doi.org/10.1080/14783363.2016.1219654

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

Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the Validity of IS Success Models: An Empirical Test and Theoretical Analysis. Information Systems Research, 13(1), 50-69. https://doi.org/10.1287/isre.13.1.50.96

Rughoobur-Seetah, S., & Zuberia, Z. A. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions. Quality Assurance in Education, 29(4), 422–444. https://doi.org/10.1108/qae-03-2021-0043

Sahin, I., & Thompson, A. (2007). Analysis of predictive factors that influence faculty members technology adoption level. Journal of Technology and Teacher Education, 15(2), 167-190.

Salkind, N. J. (2010). Encyclopedia of Research Design (1st ed.). SAGE Publication, Inc.

Sarmento, R., & Costa, V. (2016). Comparative Approaches to Using R and Python for Statistical Data Analysis (1st ed.). IGI Global Press.

Selim, H. M. (2007). Critical success factors for E-Learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396-413. https://doi.org/10.1016/j.compedu.2005.09.004

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modelling 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 Science Publishers.

Ssekakubo, G., Suleman, H., & Marsden, G. (2011, October). Issues of Adoption: Have E-Learning Management Systems Fulfilled their Potential in Developing Countries? [Paper Presentation]. Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment, Cape Town.

Tang, Y., Fan, Y., Pang, J., Zhong, S., & Wang, W. (2015). Research on Teaching Strategies of Primary School Mathematics Intelligent Classroom based on Network Learning Space. China Educational Technology, 1(2), 49-54.

Venkatesh, V., Morris, M. G., Hall, M., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). User Acceptance of Information Technology: Toward A Unified View 1. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Vrasidas, C. (2004). Issues of Pedagogy and Design in e-learning Systems. 2004 ACM Symposium on Applied Computing, 1(4), 911-915. https://doi.org/10.1145/967900.968086

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The Unified Theory of Acceptance and Use of Technology (UTAUT): A Literature Review. Journal of Enterprise Information Management, 28(3), 443-488. https://doi.org/10.1108/jeim-09-2014-0088

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

Zaied, A. N. H. (2012). An Integrated Success Model for Evaluating Information System in Public Sectors. Journal of Emerging Trends in Computing and Information Sciences, 6(3), 814-825.