A STUDY ON FACTORS AFFECTING VOCATIONAL COLLEGE STUDENTS’ WILLINGNESS TO USE ONLINE LEARNING PLATFORMS IN SHANDONG PROVINCE, CHINA

Jing Gao, Chunying Wang

Abstract


In today’s internet-driven, information-rich era, online platforms have become a key mode of learning. By examining how the intensity of online learning platform usage affects vocational college students’ willingness to use these platforms in Shandong Province, this study clarifies the factors influencing their behavior and offers valuable insights for enhancing online platform use. In this study, usage intensity is defined as the independent variable, technology readiness as the mediator, and usage intention as the dependent variable, forming a theoretical model of the factors influencing vocational college students’ willingness to use online learning platforms. The results reveal that the intensity of online learning platform usage significantly influences students’ willingness to use the platforms; Usage intensity has a notable impact on technology readiness; Technology readiness significantly affects usage intention; Technology readiness mediates the relationship between usage intensity and usage intention.

 

Article visualizations:

Hit counter


Keywords


usage intensity, technology readiness, usage intention, online learning platform, Shandong Province (China)

Full Text:

PDF

References


Aboelmaged, M. G. (2014). Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms. International Journal of Information Management, 34(5), 639-651. https://doi.org/10.1016/j.ijinfomgt.2014.05.002

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. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x

Ajzen, I., & Cote, N. G. (2008). Attitudes and the prediction of behavior. Attitudes and Attitude Change, 13, 289-305. https://psycnet.apa.org/record/2008-09973-013

Allo, M. D. G. (2020). Is the online learning good in the midst of Covid-19 Pandemic? The case of EFL learners. Jurnal Sinestesia, 10(1), 1-10. https://doi.org/10.15548/jt.v29i1.732

Bohórquez, M. R., Lara-Bocanegra, A., Teva, R., García-Fernández, J., Grimaldi-Puyana, M., & Gálvez-Ruiz, P. (2024). From e-service quality to behavioral intention to use e-fitness services post COVID-19 lockdown: When a crisis changes the social mindset. Heliyon, 10(9), https://doi.org/10.1016/j.heliyon.2024.e30382

Cho, M. H., Yanghee, K., & Choi, D. H. (2017). The effect of self-regulated learning on college students’ perceptions of community of inquiry and affective outcomes in online learning. The Internet and Higher Education, 34(7), 10-17. https://doi.org/10.1016/j.iheduc.2017.04.001

Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107-130. https://doi.org/10.1080/09639284.2021.1872035

Deng, Q., Zheng, Y., Lu, J., Zeng, Z., & Liu, W. (2021). What factors predict physicians' utilization behavior of contrast-enhanced ultrasound? Evidence from the integration of the theory of planned behavior and technology acceptance model using a structural equation modeling approach. BMC Medical Informatics and Decision Making, 21(1), 1-10. https://doi.org/10.1186/s12911-021-01540-8

Dewart, G., Corcoran, L., Thirsk, L., & Petrovic, K. (2020). Nursing education in a pandemic: Academic challenges in response to COVID-19. Nurse Educ Today, 9(2), Article e104471. https://doi.org/1.1016/j.nedt.202.104471

Durst, S., Davila, A., Foli, S., Kraus, S., & Cheng, C. F. (2022). Antecedents of technological readiness in times of crises: A comparison between before and during COVID-19. Technology in Society, 72(2), https://doi.org/10.1016/j.techsoc.2022.102195

Febrianto, P. T., Mas’udah, S., & Megasari, L. A. (2020). Implementation of online learning during the Covid-19 pandemic on Madura Island, Indonesia. International Journal of Learning, Teaching and Educational Research, 19(8), 233-254. https://doi.org/10.1016/j.iheduc.2010.10.001

Gambo, Y., & Shakir, M. Z. (2023). Evaluating students’ experiences in self-regulated smart learning environment. Education and Information Technologies, 28(1), 547-580. https://doi.org/10.1007/s10639-022-11126-0

Gause, G., Mokgaola, I. O., & Rakhudu, M. A. (2022). Technology usage for teaching and learning in nursing education: An integrative review. Curationis, 45(1), 1-9, https://doi.org/1.4102/curationis.v45i1.2261

Geng, S., Law, K. M., & Niu, B. (2019). Investigating self-directed learning and technology readiness in blending learning environment. International Journal of Educational Technology in Higher Education, 16(1), 1-22. https://doi.org/10.1186/s41239-019-0147-0

Ginting, S. S. B., Gui, A., & Deniswara, K. (2024, August). The impact of accountants’ perceptions of technology readiness and competitive intensity on technology adoption. In 2024 International Electronics Symposium (IES) (pp. 569-574). IEEE.

Hong, E., & Park, J. K. (2023). The effect of technological readiness dimensions on the adoption of autonomous vehicles: Focusing on behavioral reasoning theory. Transportation Research Part F: Traffic Psychology and Behaviour, 100(1), 101-114. https://doi.org/10.1016/j.trf.2023.11.005

Hu, S., Gu, J., Liu, H., & Huang, Q. (2017). The moderating role of social media usage in the relationship among multicultural experiences, cultural intelligence, and individual creativity. Information Technology & People, 30(2), 265-281. https://doi.org/10.1108/ITP-04-2016-0099

Jin, S. V., & Ryu, E. (2024). “Vanity fair on Instagram”: The roles of vanity, materialism, social comparison, Instagram usage intensity, and market mavenism in social commerce. Telematics and Informatics Reports, 14(6), 100-133. https://doi.org/10.1016/j.teler.2024.100133

Kim, J., Jung, H., Ko, M., & Lee, U. (2019). Goalkeeper: Exploring interaction lockout mechanisms for regulating smartphone use. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(1), 1-29. https://doi.org/10.1145/3314403

Kumar, R. (2022). E-learning programs in executive education: Effects of perceived quality and perceived value on self-regulation and motivation. Higher Education, Skills and Work-Based Learning, 12(6), 1025-1039. https://doi.org/10.1108/HESWBL-07-2022-0149

La Barbera, F., & Ajzen, I. (2020). Control interactions in the theory of planned behavior: Rethinking the role of subjective norm. Europe's Journal of Psychology, 16(3), 401-417. https://doi.org/10.5964/ejop.v16i3.2056

Lubis, F. M., Nofirman, N., Sudadi, S., Zuraida, Z., & Arief, I. (2023). Distance learning from the teachers’ perspective and students' learning experience based on self-confidence. Journal on Education, 5(4), 15723-15728. https://doi.org/10.31004/joe.v5i4.2685

Luengo-Aravena, D., Cabello, P., & Bachino, R. M. (2024). Online collaborative problem-solving as a tangible outcome of digital skills in technical and vocational higher education. Computers & Education, 2(18), 45-67. https://doi.org/10.1016/j.compedu.2024.105079

Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in Vietnam. Education and Information Technologies, 26(6), 6629-6649. https://doi.org/10.1007/s10639-021-10465-8

Mamun, M. A. A. (2022). Fostering self-regulation and engaged exploration during the learner-content interaction process: The role of scaffolding in the online inquiry-based learning environment. Interactive Technology and Smart Education, 19(4), 482-509. https://doi.org/10.1108/ITSE-11-2021-0195

Marfuah, M., Suryadi, D., Turmudi, T., & Isnawan, M. G. (2022). Providing online learning situations for in-service mathematics teachers external transposition knowledge during COVID-19 pandemic: Case of Indonesia. Electronic Journal of E-Learning, 20(1), 69-84. https://doi.org/10.1111/hpb.12289

Mariusz, D., Natalia, D., Awomir, J., Aleksandra, P., & Emilia, S. (2017). The method of coating fe 3 o 4 with carbon nanoparticles to modify biological properties of oxide measured in vitro. Journal of Aoac International, 100(4), 905-915. https://doi.org/10.5740/jaoacint.17-0165

Mark, B., Biswajit, B., Sharon, B., Amany, A., Ligi, A., & Miriam, M. (2023). The impact of COVID 19 restrictions on Australian nurse academics attitudes to technology: A survey of technology readiness index 2. Nurse Education in Practice, 8(71), Article e103719. https://doi.org/1.1016/j.nepr.2023.103719

Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2022). Acceptance analysis of NUADU as e-learning platform using the technology acceptance Model (TAM) approach. Procedia Computer Science, 18(97), 512-520. https://doi.org/10.1016/j.procs.2021.12.168

Neofytou, H., Nikas, A., & Doukas, H. (2020). Sustainable energy transition readiness: A multicriteria assessment index. Renewable and Sustainable Energy Reviews, 131(10), Article e109988. https://doi.org/10.1016/j.rser.2020.109988

Nesi, J., & Prinstein, M. J. (2015). Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms. Journal of Abnormal Child Psychology, 43(8), 1427-1438. https://doi.org/10.1007/s10802-015-0020-0

Ningsih, P. E. A., & Sari, M. N. (2021). Are learning media effective in English online learning? The students’ and teachers’ perceptions. Tarbawi: Jurnal Ilmu Pendidikan, 17(2), 173-183. https://doi.org/10.2304/elea.2006.3.4.534

Parasuraman, A., & Berry, L. (1985). A conceptual model of service quality and its implications for future research. Marketing, 49(4), 41-52. https://doi.org/10.1177/002224298504900403

Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59-74. https://doi.org/10.1177/10946705145397

Pramanik, P., Jana, R. K., & Ghosh, I. (2024). AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework. Technological Forecasting and Social Change, 205(8), https://doi.org/10.1016/j.techfore.2024.123482

Purarjomandlangrudi, A., & Chen, D. (2020). Exploring the influence of learners’ personal traits and perceived course characteristics on online interaction and engagement. Educational Technology Research and Development, 68(5), 2635-2657. https://doi.org/10.1007/s11423-020-09792-3

Rao, A. J., & Wan, K. (2020). A Study on the Influence of online learning readiness on College students' online learning engagement. Education Science, 36(2), 31-38. https://doi.org/10.3969/j.issn.1002-8064.2020.02.005

Robinson, L. (2021). Investigating critical thinking disposition, self-efficacy, self-regulation, and self-identity amongst online students. College Student Journal, 55(3), 325-337. https://doi.org/10.1177/0894845307311248

Sangwan, A., Sangwan, A., Sangwan, A., & Punia, P. (2024). A two-staged SEM: Artificial neural network approach for understanding and predicting the factors of students’ satisfaction with emergency remote teaching. Educational Technology Research and Development, 72(2), 1249-1286. https://doi.org/10.1007/s11423-023-10335-9

Tondeur, J., Scherer, R., Baran, E., Siddiq, F., Valtonen, T., & Sointu, E. (2019). Teacher educators as atekeepers: Preparing the next generation of teachers for technology integration in education. Educ, Technol, 50(3), 1189-1209, https://doi.org/1.1111/bjet.12748

Wang, I. K. H. (2021). Self-regulation. In The Routledge Handbook of the Psychology of Language Learning and Teaching (pp. 218-230). Routledge.

Yuan, C. L., Moon, H., Wang, S. M., Yu, X. L., & Kim, K. H. (2020). Study on the influencing of B2B parasocial relationship on repeat purchase intention in the online purchasing environment: An empirical study of B2B e-commerce platform. Industrial Marketing Management, 92(1), 101-110. https://doi.org/10.1016/j.indmarman.2020.11.008

Yuen, K. F., Ng, W. H., & Wang, X. Q. (2022). Switching intention in the online crowdsourced delivery environment: The influence of a platform’s technological characteristics and relational bonding strategies. Technology in Society, 72(2), Article e102167. https://doi.org/10.1016/j.techsoc.2022.102167

Zgheib, G., Daia, R. A., & Serhan, M. (2023). A contextual approach for exploring faculty readiness to teach online. Heliyon, 9(10), https://doi.org/10.1016/j.heliyon.2023.e20491

Zhang, Z. (2023). Factors influencing online learning adaptability in Chongqing college of mobile communication students during the epidemic. Procedia of Multidisciplinary Research, 1(8), Article e16. https://so09.tci-thaijo.org/index.php/PMR/article/view/3080

Zheng, J., & Li, S. (2020). What drives students’ intention to use tablet computers: An extended technology acceptance model. International Journal of Educational Research, 102, https://doi.org/10.1016/j.ijer.2020.101612

Zhu, X. M., Wang, J., & Xie, F. (2021). Research on the Relationship between Undergraduates' Exercise Commitment and Physical Exercise Behavior in Changsha City. Contemporary Sports Technology, 11(9), 208-213. https://doi.org/10.16655/j.cnki.2095-2813.2004-9757-3107




DOI: http://dx.doi.org/10.46827/ejes.v12i5.5909

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Jing Gao, Chunying Wang

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2015-2026. European Journal of Education Studies (ISSN 2501 - 1111) is a registered trademark of Open Access Publishing Group. All rights reserved.

This journal is a serial publication uniquely identified by an International Standard Serial Number (ISSN) serial number certificate issued by Romanian National Library (Biblioteca Nationala a Romaniei). All the research works are uniquely identified by a CrossRef DOI digital object identifier supplied by indexing and repository platforms. All authors who send their manuscripts to this journal and whose articles are published on this journal retain full copyright of their articles. All the research works published on this journal are meeting the Open Access Publishing requirements and can be freely accessed, shared, modified, distributed and used in educational, commercial and non-commercial purposes under a Creative Commons Attribution 4.0 International License (CC BY 4.0).