A Study of the Factors Influencing Teachers' Behavioral Intention to Use the Intelligent Center of Vocational Education (ICVE) in Chinese Higher Vocational Colleges
Abstract
Background and Aim: This study aims to explore the factors that affect the behavioral intention of teachers using the Intelligent Center of Vocational Education (ICVE) system in Chongqing higher vocational colleges. To investigate whether the quality of the system (QS), information quality (IQ), service quality (SQ), E-learning experience (XP), perceived usefulness (PU), and perceived ease of use (PEOU) have effects on ICVE behavioral intention to use (BIU).
Materials and Methods: The research builds a conceptual framework based on two core theories: the Technology acceptance model (TAM) and the DeLone and McLean information systems success model. The quantitative method and questionnaire survey were used to collect sample data from four higher vocational schools in Chongqing China. An online questionnaire was used to issue the questionnaires. After collecting data, 439 valid questionnaires were obtained. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) are used for specific data analysis. The model's goodness of fit is verified, the causal relationship between variables is confirmed, and hypothesis testing is carried out.
Results: It is found that perceived usefulness has the greatest impact on the intention to continue using, and usefulness is mainly affected by information quality and service quality. The second most important factor affecting the intention to continue use is perceived ease of use, which is significantly influenced by the E-learning experience and quality of the system.
Conclusion: Improving the quality of the system, information quality, and service quality of ICVE, and improving teachers' E-learning experience can effectively enhance teachers' behavioral intention to use ICVE.
Article Details
How to Cite
Section
Articles
Copyright & License
Copyright (c) 2025 International Journal of Sociologies and Anthropologies Science Reviews

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright on any article in the International Journal of Sociologies and Anthropologies Science Reviews is retained by the author(s) under the under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Permission to use text, content, images, etc. of publication. Any user to read, download, copy, distribute, print, search, or link to the full texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose. But do not use it for commercial use or with the intent to benefit any business.
References
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27–50. https://doi.org/10.1016/j.aci.2014.09.001
Al-Hamad, M. Q., Mbaidin, H. O., AlHamad, A. Q. M., Alshurideh, M. T., Kurdi, B. H. A., & Al-Hamad, N. Q. (2021). Investigating students’ behavioral intention to use mobile learning in higher education in UAE during the Coronavirus-19 pandemic. International Journal of Data and Network Science, 321–330. https://doi.org/10.5267/j.ijdns.2021.6.001
Alharbi, S., & Drew, S. (2014). Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems. International Journal of Advanced Computer Science and Applications, 5(1). https://doi.org/10.14569/IJACSA.2014.050120
Alhashmi, S. F. S., Salloum, S. A., & Abdallah, S. (2020). Critical Success Factors for Implementing Artificial Intelligence (AI) Projects in Dubai Government United Arab Emirates (UAE) Health Sector: Applying the Extended Technology Acceptance Model (TAM). In A. E. Hassanien, K. Shaalan, & M. F. Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 (Vol. 1058, pp. 393–405). Springer International Publishing. https://doi.org/10.1007/978-3-030-31129-2_36
Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485. https://doi.org/10.1007/s40692-016-0074-1
Alshurideh, M. T., Al Kurdi, B., AlHamad, A. Q., Salloum, S. A., Alkurdi, S., Dehghan, A., Abuhashesh, M., & Masa’deh, R. (2021). Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study. Informatics, 8(2), 32. https://doi.org/10.3390/informatics8020032
Alshurideh, M. T., Salloum, S. A., Al Kurdi, B., Abdel Monem, A., & Shaalan, K. (2019). Understanding the Quality Determinants that Influence the Intention to Use the Mobile Learning Platforms: A Practical Study. International Journal of Interactive Mobile Technologies (iJIM), 13(11), 157. https://doi.org/10.3991/ijim.v13i11.10300
Blennerhassett, R., Sudini, L., Gottlieb, D., & Bhattacharyya, A. (2019). Post‐allogeneic transplant Evans syndrome was successfully treated with daratumumab. British Journal of Haematology, 187(2). https://doi.org/10.1111/bjh.16171
Chang, C.-T., Hajiyev, J., & Su, C.-R. (2017). Examining the Students’ behavioral intention to use e-learning in Azerbaijan? The General Extended Technology Acceptance Model for E-learning Approach. Computers & Education, 111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010
Chuan-Chuan Lin, J., & Lu, H. (2000). Towards an understanding of the behavioral intention to use a web site. International Journal of Information Management, 20(3), 197–208. https://doi.org/10.1016/S0268-4012(00)00005-0
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
Di Renzo, F., Moretto, A., Battistoni, M., Beronius, A., Zilliacus, J., Hanberg, A., & Menegola, E. (2016). Skeletal craniofacial dysmorphogenesis: Suggestions for a new AOP. Toxicology Letters, 258, S302. https://doi.org/10.1016/j.toxlet.2016.06.2108
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions. Journal of Online Learning and Teaching. 11, 210-233.
Ferguson, C. L. (2017). Open Educational Resources and Institutional Repositories. Serials Review, 43(1), 34–38. https://doi.org/10.1080/00987913.2016.1274219
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., Ringle, C., et al. (2017) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd Edition, Sage Publications, Thousand Oaks
Hsia, J.-W., Chang, C.-C., & Tseng, A.-H. (2014). Effects of Individuals’ locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behavior & Information Technology, 33(1), 51–64. https://doi.org/10.1080/0144929X.2012.702284
King, T. W. (2017). Postgraduate students as OER capacitators. Open Praxis, 9(2), 223. https://doi.org/10.5944/openpraxis.9.2.566
Lee, Y.-H., Hsiao, C., & Purnomo, S. H. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5). https://doi.org/10.14742/ajet.381
Lin, K.-M., Chen, N.-S., & Fang, K. (2011). Understanding e-learning continuance intention: A negative critical incidents perspective. Behavior & Information Technology, 30(1), 77–89. https://doi.org/10.1080/01449291003752948
Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010a). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600–610. https://doi.org/10.1016/j.compedu.2009.09.009
Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010b). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600–610. https://doi.org/10.1016/j.compedu.2009.09.009
Liu, Y., & Krutkrongphan, S. (2023). The Causal Effect of Digital Leadership on Teachers' Acceptance and Use of Technology in Huaihua No.5 Middle School. Procedia of Multidisciplinary Research, 1(12),30.
Mahande, R. D., Jasruddin, J., & Nasir, N. (2019). IS Success Model for EDMODO E-learning User Satisfaction through TAM on Students. Journal of Educational Science and Technology (EST), 140–152. https://doi.org/10.26858/est.v5i2.9575
Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioral intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057–7077. https://doi.org/10.1007/s10639-021-10557-5
Megalou, E., Gkamas, V., Papadimitriou, S., Paraskevas, M., & Kaklamanis, C. (2016). Open Educational Practices: Motivating Teachers to Use and Reuse Open Educational Resources. END 2016 International Conference on Education and New Developments, Ljubljana, 42-46.
Ossiannilsson, E., Zhang, X., Wetzler, J., Gusmão, C., Aydin, C. H., Jhangiani, R., Glapa-Grossklag, J., Makoe, M., & Harichandan, D. (2020). From Open Educational Resources to Open Educational Practices: For resilient sustainable education. Distances et Médiations Des Savoirs, 31. https://doi.org/10.4000/dms.5393
Reed, W. M., Oughton, J. M., Ayersman, D. J., Ervin, J. R., & Giessler, S. F. (2000). Computer experience, learning style, and hypermedia navigation. Computers in Human Behavior, 16(6), 609–628. https://doi.org/10.1016/S0747-5632(00)00026-1
Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019). Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467
Sylvia, C., & Abdurachman, E. (2018). E-LEARNING acceptance analysis using technology acceptance model (tam) (case study: stmik mikroskil). Journal of Theoretical and Applied Information Technology, 15, 19-30.
Tarhini, A., Elyas, T., Akour, M. A., & Al-Salti, Z. (2016). Technology, Demographic Characteristics, and E-Learning Acceptance: A Conceptual Model Based on Extended Technology Acceptance Model. Higher Education Studies, 6(3), 72. https://doi.org/10.5539/hes.v6n3p72
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behavior in developing countries: A structural equation model. Computers in Human Behavior, 41, 153–163. https://doi.org/10.1016/j.chb.2014.09.020
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. (2003). Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly, 15(1), 125. https://doi.org/10.2307/249443
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Wang, Y.-S., Wang, H.-Y., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(4), 1792–1808. https://doi.org/10.1016/j.chb.2005.10.006
Y. Liu & Krutkrongphan, n.d