Study on the Factors Influencing the Usage Behavior of International Education Cloud Platform in China
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Abstract
Purpose: The purpose of this research is to investigate the factors influencing the usage behavior of International Education Cloud Platforms (IECPs) in China. The study employed a quantitative method, utilizing a questionnaire for data collection from the target population. To ensure content validity and reliability, Item-Objective Congruence (IOC) and a pilot test of Cronbach's Alpha were conducted before distributing the questionnaire.Data analysis involved Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to assess the model's goodness of fit and confirm the causal relationships among variables for hypothesis testing. The results indicated that the research conceptual model effectively predicted and explained the actual usage (AU) of IECPs in higher vocational and technical education. All seven hypotheses proposed were supported, meeting the research objectives. The study suggests that developers of IECP courses and management in higher vocational education institutions should concentrate on enhancing the quality factors of IECP. This focus would enable students to perceive the system as useful, fostering a positive attitude and behavioral intention toward using IECP.
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References
Abdulrahim, H., & Mabrouk, F. (2020). COVID-19 and the digital transformation of Saudi higher education. Asian Journal of Distance Education, 15(1), 291-306.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683.
Al-Mamary, Y. H. S., & Shamsuddin, A. (2015). The impact of management information systems adoption in managerial decision making: A review. Management Information Systems, 8(4), 10-17.
Al-Mamary, Y. H. S., & Alraja, M. M. (2022). Understanding entrepreneurship intentions and behavior in the light of TPB model from the digital entrepreneurship perspective. International Journal of Information Management Data Insights, 2(2), 100106.
Awang, Z. (2012). Structural Equation Modeling Using AMOS Graphic. Penerbit Universiti Teknologi MARA.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
Bervell, B., Nyagorme, P., & Arkorful, V. (2020). LMS-enabled blended learning use intentions among distance education tutors: Examining the mediation role of attitude based on technology-related stimulus-response theoretical framework. Contemporary Educational Technology, 12(2), 273.
Catherine, N., Geofrey, K. M., Moya, M. B., & Aballo, G. (2018). Effort expectancy, performance expectancy, social influence and facilitating conditions as predictors of behavioral intentions to use ATMs with fingerprint authentication in Ugandan banks. Global Journal of Computer Science and Technology, 17(5), 5-22.
Cigdem, H., & Ozturk, M. (2016). Factors affecting students’ behavioral intentions to use LMS at a Turkish post-secondary vocational school. International Review of Research in Open and Distributed Learning, 17(3), 276-295.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
Kaye, S. A., Lewis, I., Forward, S., & Delhomme, P. (2020). A priori acceptance of highly automated cars in Australia, France, and Sweden: A theoretically-informed investigation guided by the TPB and UTAUT. Accident Analysis and Prevention, 133, 105441.
Krueger, N. F., Reilly, M. D., & Carsrud, A. L. (2000). Competing models of entrepreneurial intentions. Journal of Business Venturing, 15(5-6), 411-432.
Mensah, I. K. (2019). Factors influencing the intention of university students to adopt and use e-government services: an empirical evidence in China. Sage Open, 9(2), 2158244019855823.
Pan, Y., Huang, Y., Kim, H., & Zheng, X. (2021). Factors influencing students’ intentions to adopt online interactive behaviors: Merging the theory of planned behavior with cognitive and motivational factors. The Asia-Pacific Education Researcher, 32(1), 1-10.
Pedroso, R. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396-413.
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935-943.
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Comprehensive Psychiatry, 48(3), 326-333.
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, http://dx.doi.org/10.2307/30036540.
Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: U.S. vs. China. Journal of Global Information Technology Management, 13(1), 5-27.
Wu, J. H., & Wang, S. C. (2006). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 43(5), 711-729.