![]() Examining the integrated influence of fairness and quality on learners' Satisfaction and Web-based learning continuance intention. Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Turkish Online Journal of Distance Education, 14(1), 85–101.Ĭhao, C. Identifying factors that contribute to the Satisfaction of students in e-learning. Ĭalli, L., Balcikanli, C., Calli, F., Cebeci, H. 50 Online Education Statistics: 2020/2021 Data on Higher Learning & Corporate Training. Comparative fit indexes in structural models. Interactive Technology and Smart Education, 13(4), 289 - 304. Demographic determinants of usefulness of e-learning tools among students of public administration. ![]() Prentice-Hall.Īristovnik, A., Keržič, D., Tomaževič, N., & Umek, L. Understanding Attitudes and Predicting Social Behavior. Open University Press.Ījzen, I., & Fishbein, M. Attitudes, Personality And Behaviour (2nd ed.). International Journal of Education and Development Using Information and Communication Technology, 9(2), 4–18.Ījzen, I. Exploring students acceptance of e-learning using Technology Acceptance Model in Jordanian universities. A proposed model for evaluating the success of WebCT course content management system. Henceforth, academic practitioners and universities are recommended to provide an effective system, high service support standard, and promote the benefits of the online learning system to students.Īdeyinka, T., & Mutula, S. Conversely, there were no supported relationships of service quality and behavioral intention, followed by information quality and behavioral intention, perceived usefulness and attitude toward use, and satisfaction and behavioral intention. The findings revealed that the strongest significant relationship was the attitude toward use and behavioral intention, followed by behavioral intention and actual use, system quality and behavioral intention, and perceived ease of use and attitude toward use. Afterward, data analysis was carried out employing descriptive analysis, confirmation factor analysis (CFA), and structural equation modeling (SEM). Before the data collection, an index of item objective congruence (IOC) was used to validate items in the questionnaire, and Cronbach's Alpha Coefficient reliability test was used to measure the reliability of the questionnaire by conducting a pilot test with 40 participants. The sampling method used was nonprobability sampling, including judgmental sampling, quota sampling, and convenience sampling. The researcher applied a quantitative approach to collect data by distributing online questionnaires to 500 fourth-year students in three private universities. The conceptual framework presents key constructs, including perceived ease of use, perceived usefulness, information quality, system quality, service quality, attitude toward use, satisfaction, behavioral intention, and actual use. This research aims to determine factors influencing online learning usage of students in higher education in Sichuan, China. Perceived ease of use, perceived usefulness, information quality, system quality, service quality Abstract
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