Influencing Factors of Art Students' Behavioral Intention on XueXitong Usage Among Three Universities, Chengdu, China

Jiang Yi
Thailand
Satha Phongsatha
Thailand
Keywords: Higher education, Art major, XueXiTong, Behavioral intention, Actual usage
Published: Jan 24, 2025

Abstract

Background and Aim: Facing the increasingly prevalent trend of online education among art students in China, This study aims to investigate the factors influencing the behavioral intentions of art students at three universities in Chengdu toward the use of XueXiTong. Drawing on the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), a conceptual framework was established. Nine variables were selected for this study: Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude (ATT), Habit (H), Performance Expectancy (PE), Effort Expectancy (EE), Behavioral Intention (BI), Subjective Norm (SN), and Actual Usage (AU).


Materials and Methods: The researcher used a quantitative method, distributing questionnaires to 500 respondents through online channels. The data were analyzed using confirmatory factor analysis and structural equation modeling to validate the goodness of fit of the model, determine causal relationships between variables, and perform hypothesis testing.


Results: The data analysis results indicated that all variables had a significant impact on the behavioral intention of art major students to use XueXiTong in Chengdu, China, and all hypotheses were supported. Among the variables, behavioral intention was found to have the greatest effect on actual usage.


Conclusion: The study findings suggest that enhancing students' perceived ease of use and perceived usefulness of XueXiTong, along with improving their attitude, subjective norm, effort expectancy, performance expectancy, and habit, significantly influences their behavioral intention and actual usage of the platform. These findings emphasize the importance of optimizing these factors for effectively promoting the use of XueXiTong among art students at three universities in Chengdu, China.

Article Details

How to Cite

Yi, J., & Phongsatha, S. . (2025). Influencing Factors of Art Students’ Behavioral Intention on XueXitong Usage Among Three Universities, Chengdu, China. International Journal of Sociologies and Anthropologies Science Reviews, 5(1), 837–852. https://doi.org/10.60027/ijsasr.2025.5505

Section

Articles

References

Ain, N., Kaur, K., & Waheed, M. (2015). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 1, 16.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in the context of Yemen. Mediterranean Journal of Social Sciences, 6(4),1-10.

Alwahaishi, S. (2021). Student use of E-Learning during the coronavirus pandemic: an extension of UTAUT to trust and perceived risk. International Journal of Distance Education Technologies (IJDET), 19(4), 72-90.

Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18, 659-673.

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

Azizi, S. M., Roozbahani, N., & Khatony, A. (2020). Factors affecting the acceptance of blended learning in medical education: application of UTAUT2 model. BMC Medical Education, 20, 1-9.

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

Chen, Y., Teng, F., & Yu, Z. (2018). A Study on the User’Actual Use and the Behaviors Influencing Factors in Online Learning Platform of Coursera. Adv. Educ, 8, 254-268.

Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064.

Cheong, J. H., & Park, M. (2005). Mobile Internet acceptance in Korea. Internet Research, 15, 125-140. Retrieved from http://www.emeraldinsight.com/loi/intr

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural equation modeling, 9(2), 233-255.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and users' acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111 - 1132.

DeLone, W. H., & McLean, E. R. (2016). Information systems success measurement. Foundations and Trends® in Information Systems, 2(1), 1-116.

DiGuiseppi, G. T., Meisel, M. K., Balestrieri, S. G., Ott, M. Q., Cox, M. J., Clark, M. A., & Barnett, N. P. (2018). Resistance to peer influence moderates the relationship between perceived (but not actual) peer norms and binge drinking in a college student social network. Addictive behaviors, 80, 47-52.

Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with the task–technology fit constructs. Information & Management, 36(1), 9-21.

Dong, L., Ji, T., & Zhang, J. (2023). Motivational Understanding of MOOC Learning: The Impacts of Technology Fit and Subjective Norms. Behavioral Sciences, 13(2), 98.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.

Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning acceptance among university students: An integrated model of TPB and TAM. International Review of research in open and distributed learning, 20(3), 141-164. https://doi.org/10.19173/irrodl.v20i4.4061

Goto, J., & Munyai, A. (2022). The acceptance and use of online learning by law students in a South African University: An Application of the UTAUT2 Model. The African Journal of Information Systems, 14(1), https://digitalcommons. kennesaw.edu/ajis/vol14/iss1/3

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis. 6th edition. Harlow, England: Pearson Education.

Handoko, B. L. (2019, July). Application of UTAUT theory in higher education online learning. In Proceedings of the 2019 10th International Conference on E-business, Management and Economics (pp. 259-264).

Heijden, H. (2003). Factors influencing the usage of website: the case of a generic portal in The Netherlands. Information & Management, 40, 541-549.

Ikhsan, R. B., & Prabowo, H. A. R. T. I. W. I. (2021). Drivers of the mobile-learning management system’s actual usage: Applying the output model. ICIC express letters. Part B, Applications: an international journal of research and surveys, 12(11), 1067-1074.

Jameel, A. S., Abdalla, S. N., & Karem, M. A. (2020, November). Behavioral Intention to Use E-Learning from student's perspective during the COVID-19 Pandemic. In 2020 2nd Annual International Conference on Information and Sciences (AiCIS) (pp. 165-171). IEEE.

Karjaluoto, H., Mattila, M., & Pento, T. (2002). Factors underlying attitude formation towards online banking in Finland. International journal of bank marketing, 20(6), 261-272.

Khoa, B. T., Ha, N. M., Nguyen, T. V. H., & Bich, N. H. (2020). Lecturers' adoption to use the online Learning Management System (LMS): Empirical evidence from TAM2 model for Vietnam. Ho Chi Minh City Open University Journal of Science-Economics and Business Administration, 10(1), 3-17.

Kim, B. (2012). The diffusion of mobile data services and applications: Exploring the role of habit and its antecedents. Telecommunications Policy, 36, 69–81. doi: 10.1016/j.telpol.2011.11.011

Kim, B. G., Park, S. C., & Lee, K. J. (2007). A structural equation modeling of the Internet acceptance in Korea. Electronic Commerce Research and Applications, 6(4), 425-432.

Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755.

Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42, 1095–1104.

Li, X. (2009). Review of distance education used in higher education in China. Asian Journal of Distance Education, 7(2), 30-41. https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/146

Liao, H. L., & Lu, H. P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405-1416.

Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 31, 705-737. https://doi.org/10.2307/25148817

Lin, S., Zimmer, J. C., & Lee, V. (2013). Podcasting acceptance on campus: The differing perspectives of teachers and students. Computers & Education, 68, 416-428.

Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599-607.

Masrom, M. (2007) Technology Acceptance Model and E-Learning. 12th International Conference on Education, 21-24 May 2007, Brunei Darussalam: Universiti Brunei Darussalam, 1-10.

Muhamad Safiih, L., & Azreen, N. (2016). Confirmatory Factor Analysis Approach. Malaysian Journal of Mathematical Sciences

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43 (3), 37-40.

Rabaa'i, A. A. (2016). Extending the technology acceptance model (TAM) to assess students' behavioral intentions to adopt an e-learning system: The case of Moodle as a learning tool. Journal of emerging trends in engineering and applied sciences, 7(1), 13-30.

Raman, A., & Don, Y. (2013). Preservice teachers' acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164.

Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445-128462.

Sharif, A., Afshan, S., & Qureshi, M. A. (2019). Acceptance of learning management system in university students: an integrating framework of modified UTAUT2 and TTF theories. International Journal of Technology Enhanced Learning, 11(2), 201-229.

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286.

Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet Original Research Article. Computers in Human Behavior, 25, 1343-1354. doi: 10.1016/j.chb.2009.06.001

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.

Singh, M., & Matsui, Y. (2017). How long tail and trust affect online shopping behavior: An extension to UTAUT2 framework. Pacific Asia Journal of the Association for Information Systems, 9(4), 2. DOI: 10.17705/1pais.09401.

Soper, D.S. (2020) A-Priori Sample Size Calculator for Structural Equation Models [Software].

http://wwwdanielsopercom/statcalc

Tandon, U. (2020). Factors influencing adoption of online teaching by school teachers: A study during COVID-19 pandemic. Journal of Public Affairs, 21(4), e2503-e2503.

Tarhini, A., Masa’deh, R. E., 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.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.

Terblanche, W., Lubbe, I., Papageorgiou, E., & van der Merwe, N. (2023). Acceptance of e-learning applications by accounting students in an online learning environment at residential universities. South African Journal of Accounting Research, 37(1), 35-61.

Thongsri, N., Shen, L., & Bao, Y. (2020). Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in China. Innovations in Education and Teaching International, 57(5), 577-589.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.

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.

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. Management Information Systems Quarterly, 36(1), 157–178.

Widjaja, H. A. E., Santoso, S. W., & Petrus, S. (2019, August). The enhancement of the learning management system in teaching teaching-learning process with the UTAUT2 and trust model. In 2019 International Conference on Information Management and Technology (ICIMTech) (Vol. 1, pp. 309-313). IEEE.

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in human behavior, 67, 221-232.

Wu, B., & Zhang, C. Y. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027e1038.

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

Xia, W. (2017, July). Analysis of Industry University Research Institute Talents Training Mode in College Art Major. In 2017 7th International Conference on Social Network, Communication and Education (SNCE 2017) (pp. 923-927). Atlantis Press.

Yang, H. H., & Su, C. H. (2017). Learner behavior in a MOOC practice-oriented course: In empirical study integrating TAM and TPB. International Review of Research in Open and Distributed Learning, 18(5), 35-63.

Yaşlıoğlu, M., & Yaşlıoğlu, D. T. (2020). How and when to use which fit indices? A practical and critical review of the methodology. Istanbul Management Journal, (88), 1-20.

Zhang, X. (2020, March). Thoughts on large-scale long-distance web-based teaching in colleges and universities under novel coronavirus pneumonia epidemic: a case of Chengdu University. In 4th International Conference on Culture, Education and Economic Development of Modern Society (ICCESE 2020) (pp. 1222-1225). Atlantis Press.