Factors Impacting Behavioral Intention to Use Online Learning of Junior College Students in a Private Vocational University in Chengdu, China

Zhong Yangbaixue

doi:10.14456/shserj.2025.6

Authors

Zhong Yangbaixue

DOI:

https://doi.org/10.14456/shserj.2025.6

CITATION

DOI: 10.14456/shserj.2025.6

Published: 2025-03-21

Keywords:

E-Learning Quality, Perceived Usefulness, Perceived Ease of Use, Behavioral Intention

Abstract

Purpose: This study aims to determine factors impacting behavioral intention of students painting majors in a private vocational university in Chengdu, China. The conceptual framework contains perceived ease of use, responsiveness, reliability, perceived usefulness, e-learning quality, hedonic motivation, facilitation condition, social influence, and behavioral intention. Research design, data, and methodology: Quantitative methods were employed to survey a cohort of 500 participants. Prior to data collection, the study ensured the validity and reliability through the assessment of the Item-Objective Congruence (IOC) index and the calculation of Cronbach's Alpha during a pilot test involving 50 participants. Confirmatory factor analysis (CFA) and Structural Equation Modeling (SEM) were to assess and conduct statistical data processing. Results: perceived usefulness emerges as the most influential factor affecting behavioral intention. Furthermore, it is observed that perceived ease of use significantly contributes to perceived usefulness. Additionally, the study affirms the substantial impacts of reliability and responsiveness on the quality of the e-learning experience. Lastly, hedonic motivation, facilitating conditions, social influences, and the perceived quality of e-learning all collectively affect students' behavioral intentions in the online learning environment. Conclusions: The author elaborates on the relevant factors that affect the online learning behavior intention and how to improve their behavior intention, e-learning quality, and perceived usefulness.

Author Biography

Zhong Yangbaixue

Chengdu Vocational University of the Arts, China.

References

Abu Gharrah, A., & Aljaafreh, A. (2021). Why students use social networks for education: Extension of UTAUT2. Journal of Technology and Science Education, 11(1), 53.

https://doi.org/10.3926/jotse.1081

Ainur, A. K., Deni, S. M., Jannoo, Z., & Yap, B. W. (2017). Sample size and non-normality effects on goodness of fit measures in structural equation models. Pertanika Journal of Science and Technology, 25(2), 575-586

Al-Busaidi, K. A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full E-lEarning. Behavior & Information Technology, 32(11), 1168-1176. https://doi.org/10.1080/0144929x.2013.774047

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

Alkhattabi, M., Neagu, D., & Cullen, A. (2011). Assessing information quality of E-lEarning systems: A web mining approach. Computers in Human Behavior, 27(2), 862-873.

https://doi.org/10.1016/j.chb.2010.11.011

Alkis, N., Coskunçay, D. F., & Yildirim, S. Ö. (2014). A systematic review of technology acceptance model in E-LEarning context. Proceedings of the XV International Conference on Human Computer Interaction. 55, 1-5. https://doi.org/10.1145/2662253.2662308

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 1-10.

https://doi.org/10.5901/mjss.2015.v6n4s1p268

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411423.

https://doi.org/10.1037/0033-2909.103.3.411

Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.

Bao, W. (2020). COVID ‐19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113-115.

https://doi.org/10.1002/hbe2.191

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921

Budu, K. W., Yinping, M., & Mireku, K. K. (2018). Investigating the effect of behavioral intention on E-learning systems usage: Empirical study on tertiary education institutions in Ghana. Mediterranean Journal of Social Sciences, 9(3), 201-216. https://doi.org/10.2478/mjss-2018-0062

Cheng, B., Wang, M., Moormann, J., Olaniran, B. A., & Chen, N. S. (2012). The effects of organizational learning environment factors on e-learning acceptance. Computers and Education, 58(3), 885-899.

Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot E-sErvicE and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595. https://doi.org/10.1016/j.jbusres.2018.10.004

Cyr, D., Head, M., & Ivanov, A. (2009). Perceived interactivity leading to E-loyalty: Development of a model for cognitive–affective user responses. International Journal of Human-Computer Studies, 67(10), 850-869. https://doi.org/10.1016/j.ijhcs.2009.07.004

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., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982

Dhawan, S. (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems, 49(1), 5-22. https://doi.org/10.1177/0047239520934018

El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of E-lEarning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763.

https://doi.org/10.1007/s11423-016-9508-8

Feng, D., Xiang, C., Vongurai, R., & Pibulcharoensit, S. (2022). Investigation on Satisfaction and Performance of Online Education Among Fine Arts Major Undergraduates in Chengdu Public Universities. AU-GSB E-JOURNAL, 15(2), 169-177. https://doi.org/10.14456/augsbejr.2022.82

Folkes, V. S. (1988). Recent attribution research in consumer behavior: A review and new directions. Journal of Consumer Research, 14(4), 548. https://doi.org/10.1086/209135

Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519

George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and Reference. 11.0 Update (4th ed.). Allyn & Bacon.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis (7th ed.). Prentice Hall.

Hair, J. F., Babin, A., Money, A., & Samouel, P. (2003). Essentials of business research methods (3rd ed.). John Wiley & Sons

Haryanto, H., & Kaltsum, H. U. (2016). E-learning Program Adoption: Technology Acceptance Model Approach Vol. 02. https://jurnal.uns.ac.id/ictte/article/view/7180/6394.

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487

Heggart, K., & Yoo, J. (2018). Getting the most from Google classroom: A pedagogical framework for tertiary educators. Australian Journal of Teacher Education, 43(3), 140-153. https://doi.org/10.14221/ajte.2018v43n3.9

Herdiyanti, F. A., & Puspitasari, N. (2020). The e-Learning Quality Model to Examine Students' Behavioral Intention to Use. International Journal of Data and Network Science, 6(3), 669-682.

Hew, T., & Kadir, S. L. (2017). The drivers for cloud-based virtual learning environment: examining the moderating effect of school category. Internet Research, 27(4), 942-973.

Hossain, M. A., Hasan, M. I., Chan, C., & Ahmed, J. U. (2017). Predicting user acceptance and continuance behavior towards location-based services: The moderating effect of facilitating conditions on behavioral intention and actual use. Australasian Journal of Information Systems, 21, 1-10. https://doi.org/10.3127/ajis.v21i0.1454

Hu, J., & Lai, J. (2019). Factors Associated with Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019. JAMA Netw Open, 3(3), e203976. https://doi.org/10.1001/jamanetworkopen.2020.3976

Hu, X., Lei, L. C., Li, J., Iseli-Chan, N., Siu, F. L., & Chu, S. K. (2016). Access Moodle using mobile phones: Student usage and perceptions. Mobile Learning Design, 2(1), 155-171.

https://doi.org/10.1007/978-981-10-0027-0_10

Johnson, R. D., Gueutal, H., & Falbe, C. M. (2009). Technology, trainees, metacognitive activity, and E‐lEarning effectiveness. Journal of Managerial Psychology, 24(6), 545-566. https://doi.org/10.1108/02683940910974125

Khan, R. A., & Qudrat-Ullah, H. (2021). Adoption of LMS in higher educational institutions of the Middle East (1st ed.). Springer.

Lavrakas, P. J. (2008). Encyclopedia of survey research methods (1st ed.). Sage Publications.

Lee, H. Y., Lee, Y., & Kwon, D. (2005). The intention to use computerized reservation systems: The moderating effects of organizational support and supplier incentive. Journal of Business Research, 58(11), 1552-1561. https://doi.org/10.1016/j.jbusres.2004.07.008

Liao, C., & Tsou, C. (2009). User acceptance of computer-mediated communication: The SkypeOut case. Expert Systems with Applications, 36(3), 4595-4603. https://doi.org/10.1016/j.eswa.2008.05.015

Loh, C., Wong, D. H., Quazi, A., & Kingshott, R. P. (2016). Re-examining students’ perception of E-lEarning: An Australian perspective. International Journal of Educational Management, 30(1), 129-139. https://doi.org/10.1108/ijem-08-2014-0114

Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The influence of values on E-learning adoption. Computers and Education, 141, 103617. https://doi.org/10.1016/j.compedu.2019.103617

Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-Web context. Information & Management, 38(4), 217-230. https://doi.org/10.1016/s0378-7206(00)00061-6

Mtebe, J. S., & Raphael, C. (2018). Key factors in learners’ satisfaction with the E-lEarning system at the University of Dar es Salaam, Tanzania. Australasian Journal of Educational Technology, 34(4), 1-10.https://doi.org/10.14742/ajet.2993

Muqtadiroh, F. A., Herdiyanti, A., & Puspitasari, N. (2020). The e-learning quality model to examine students’ behavioral intention to use online learning platform in a higher education institution. Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika, 6(2), 176-183.

Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25(5), 4139-4155. https://doi.org/10.1007/s10639-020-10157-9

Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43, 37-40. https://doi.org/10.1590/0101-60830000000081

Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-Based Virtual Learning Environments: A Research Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training. MIS Quarterly, 25, 401-426. http://dx.doi.org/10.2307/3250989

Pituch, K. A., & Lee, Y. (2006). The influence of system characteristics on E-lEarning use. Computers & Education, 47(2), 222-244. https://doi.org/10.1016/j.compedu.2004.10.007

Rudhumbu, N. (2022). A Gender-Based Analysis of Classroom Interaction Practices the Effect Thereof on University Students’ Academic Performance. International Journal of Learning, Teaching and Educational Research, 21(5), 22-45 https://doi.org/10.26803/ijlter.21.5.2

Ryan, R. M., & Deci, E. L. (2011). A self-determination theory perspective on social, institutional, cultural, and economic supports for autonomy and their importance for well-being. In V. I. Chirkov, R. M. Ryan, & K. M. Sheldon (Eds.), Human autonomy in cross-cultural context: Perspectives on the psychology of agency, freedom, and well-being (pp. 45-64). Springer Science + Business Media. https://doi.org/10.1007/978-90-481-9667-8_3

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, 99, 128445-128462.

https://doi.org/10.1109/access.2019.2939467

Salloum, S. A., & Shaalan, K. (2018). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. International Conference on Advanced Intelligent Systems and Informatics, 469-480.

https://doi.org/ 10.1007/978-3- 319-99010-1_43

Sandholtz, J. H., Ringstaff, C., & Dwyer, D. C. (1992). undefined. Journal of Educational Computing Research, 8(4), 479-505. https://doi.org/10.2190/y5ne-v9rq-fd63-wc2n

Serenko, A. (2008). A model of user adoption of interface agents for email notification. Interacting with Computers, 20(4-5), 461-472. https://doi.org/10.1016/j.intcom.2008.04.004

Sharma, G. P., Verma, R. C., & Pathare, P. B. (2005). Thin-layer infrared radiation drying of onion slices. Journal of Food Engineering, 67(3), 361-366. https://doi.org/10.1016/j.jfoodeng.2004.05.002

Shroff, R. H., Deneen, C. C., & Ng, E. M. (2011). Analysis of the technology acceptance model in examining students' behavioural intention to use an E-portfolio system. Australasian Journal of Educational Technology, 27(4). https://doi.org/10.14742/ajet.940

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.

Stoian, C. E., Fărcașiu, M. A., Dragomir, G.-M., & Gherheș, V. (2022). Transition from Online to Face-to-Face Education after COVID-19: The Benefits of Online Education from Students’ Perspective. Sustainability, 14(19), 12812. https://doi.org/10.3390/su141912812

Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The Battle of brain vs. heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2. International Journal of Information Management, 46, 222-235. https://doi.org/10.1016/j.ijinfomgt.2019.01.008

Tarhini, A., Masa’deh, R., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of E-lEarning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182. https://doi.org/10.1108/jieb-09-2016-0032

Teo, T., Zhou, M., Fan, A. C., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67(3), 749-766. https://doi.org/10.1007/s11423-019-09650-x

Van den Broeck, E., Zarouali, B., & Poels, K. (2019). Chatbot advertising effectiveness: When does the message get through?. Computers in Human Behavior, 98, 150-157. https://doi.org/10.1016/j.chb.2019.04.009

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. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412

Virvou, M., & Katsionis, G. (2008). On the usability and likeability of virtual reality games for education: The case of VR-ENGAGE. Computers & Education, 50(1), 154-178.

https://doi.org/10.1016/j.compedu.2006.04.004

Widaman, K. F., & Thompson, J. S. (2003). On specifying the null model for incremental fit indices in structural equation modeling. Psychological Methods, 8(1), 16-37.

https://doi.org/10.1037/1082-989X.8.1.16

Wu, J., & Liu, W. (2013). An empirical investigation of the critical factors affecting students’ satisfaction in EFL blended learning. Journal of Language Teaching and Research, 4(1), 176-185. https://doi.org/10.4304/jltr.4.1.176-185

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & management, 43(6), 728-739.

https://doi.org/10.1016/j.im.2006.05.002

Yusuf, N., & AL-Banawi, N. (2013). The Impact Of Changing Technology: The Case Of E-Learning. Contemporary Issues in Education Research (CIER), 6(2), 173.

https://doi.org/10.19030/cier.v6i2.7726

Zhang, Y., Fang, Y., Wei, K., & Wang, Z. (2012). Promoting the intention of students to continue their participation in E‐lEarning systems. Information Technology & People, 25(4), 356-375. https://doi.org/10.1108/09593841211278776

Zhou, L., Meng, W., Wu, S., & Cheng, X. (2023). Development of Digital Education in the Age of Digital Transformation: Citing China’s Practice in Smart Education as a Case Study. Science Insights Education Frontiers, 14(2), 2077-2092. 10.15354/sief.23.or095.