Examining Behavioral Intention and Use Behavior in Online Learning Among Students of Vocal Language and Art College in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2025.73Keywords:
Online Learning, Tencent Meeting, Behavioral Intention, Use BehaviorAbstract
Purpose: This study aims to explore the factors affecting students' behavioral intention and use behavior in the context of online learning at a private university specializing in vocal language and art college in Chengdu, China. The conceptual framework incorporated perceived ease of use, usefulness, attitude toward use, social influence, trust, behavioral intention, and use behavior. Research design, data, and methodology: . The researcher employed a quantitative survey methodology to distribute questionnaires among the students at the targeted college. 472 valid data were assessed. Item-Objective Congruence (IOC) was evaluated for content validity. 40 students were involved in the pilot test for Cronbach’s Alpha reliability test. Confirmatory Factor Analysis (CFA) was conducted to ensure the construct validity of the relationship between the collected data and the proposed conceptual framework. Furthermore, Structural Equation Modeling (SEM) was utilized to assess the significant factors affecting the variables related to behavioral intention. Results: The results of the study confirmed all seven hypotheses. Notably, the results of this study testing the hypotheses indicate that behavioral intention significantly impacts use behavior. Conclusions: Future improvements for online learning platforms should focus on introducing interactive tutorials and guides, incorporating specialized tools tailored to creative fields, and bolstering feedback mechanisms.
References
Agudo-Peregrina, Á. F., Hernandez-Garcia, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314.
Alfraih, M. M., & Alanezi, F. S. (2016). Accounting students’ perceptions of effective faculty attributes. Journal of International Education in Business, 9(2), 123-142.
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 2(1), 11-23. https://doi.org/10.5901/mjss.2015.v6n4s1p268
Aslam, W., Ham, M., & Arif, I. (2017). Consumer behavioral intentions towards mobile payment services: An empirical analysis in Pakistan. Trziste Market, 29(2), 161-176.
https://doi.org/10.22598/mt/2017.29.2.161
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA
Awwad, M. S., & Al-Majali, S. M. (2015). Electronic library services acceptance and use: An empirical validation of unified theory of acceptance and use of technology. The Electronic Library, 33(6), 1100-1120. https://doi.org/10.1108/el-03-2014-0057
Bagozzi, R. P., & Lee, K. H. (2002). Multiple routes for social influence: The role of compliance, internalization, and social identity. Social Psychology Quarterly, 65(3), 226-247. https://doi.org/10.2307/3090121
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
Chao, C. M., & Yu, T. K. (2019). The moderating effect of technology optimism: How it affects students’ weblog learning. Online Information Review, 43(1), 161-180.
https://doi.org/10.1108/oir-11-2016-0316
Chauhan, S. (2015). Acceptance of mobile money by poor citizens of India: Integrating trust into the technology acceptance model. Info, 17(3), 58-68. https://doi.org/10.1108/info-02-2015-0018
Cheng, E. W., Chu, S. K., & Ma, C. S. (2019). Students’ intentions to use PBWorks: A factor-based PLS-SEM approach. Information and Learning Sciences, 120(7/8), 489-504.
https://doi.org/10.1108/ils-05-2018-0043
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
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
De Haan, E., Kannan, P., Verhoef, P. C., & Wiesel, T. (2018). Device switching in online purchasing: Examining the strategic contingencies. Journal of Marketing, 82(5), 1-19.
https://doi.org/10.1509/jm.17.0113
Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231. https://doi.org/10.1108/apjml-06-2013-0061
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
Gu, J. C., Lee, S. C., & Suh, Y. H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605-11616. https://doi.org/10.1016/j.eswa.2009.03.024
Handoko, B. L. (2019). Application of UTAUT theory in higher education online learning. Proceedings of the 2019 10th International Conference on E-business, Management and Economics, 259-264.
Hsu, H. Y., Kwok, O. M., Lin, J. H., & Acosta, S. (2015). Detecting misspecified multilevel structural equation models with common fit indices: A Monte Carlo study. Multivariate Behavioral Research, 50(2), 197-215. https://doi.org/10.1080/00273171.2014.977429
Huang, G., & Yuan, X. (2020). New edition Tutorial of Education Statistics and Measurement Evaluation. East China Normal University Press.
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585-598. https://doi.org/10.1108/02640470710829569
Huang, Y. M. (2016). The factors that predispose students to continuously use cloud services: Social and technological perspectives. Computers & Education, 97, 86-96.
https://doi.org/10.1016/j.compedu.2016.02.016
Ikhlash, M., & Tama, H. F. (2021). Behavior Intention to Use dan Use Behavior Mahasiswa dalam Menggunakan E-Learning dimasa Covid-19. Jurnal Akuntansi, Ekonomi Dan Manajemen Bisnis, 9(2), 136-142. https://doi.org/10.30871/jaemb.v9i2.3395
Keong, M. L., Ramayah, T., Kurnia, S., & Chiun, L. M. (2012). Explaining intention to use an enterprise resource planning (ERP) system: An extension of the UTAUT model. Business Strategy Series, 13(4), 173-180. https://doi.org/10.1108/17515631211246249
Khan, R. A., & Qudrat-Ullah, H. (2021). Adoption of LMS in higher educational institutions of the Middle East. Springer.
Kini, A., & Choobineh, J. (1998). Trust in electronic commerce: definition and theoretical considerations. Proceedings of the Thirty-First Hawaii International Conference on System Sciences, 51-61.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Ku, C. H. (2009). Extending the technology acceptance model using perceived user resources in higher education web-based online learning courses [Doctoral Dissertation]. University of Central Florida.
Liao, C. H., Tsou, C. W., & Shu, Y. C. (2008). The roles of perceived enjoyment and price perception in determining acceptance of multimedia-on-demand. International Journal of Business and Information, 3(1), 27-52.
Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252-260. https://doi.org/10.1016/j.ijinfomgt.2010.07.006
Lu, Y., Yang, S., Chau, P. Y., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management, 48(8), 393-403. https://doi.org/10.1016/j.im.2011.09.006
Masrom, M. (2007, May 21-24). Technology Acceptance Model and E-Learning. Proceedings of the 12th International Conference on Education, Universiti Brunei Darussalam. 1-10.
Moore, M. (2012). Hoyle CFA Chapter - Final. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (pp. 361-379). Guilford Press.
Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Perry, J. L., Nicholls, A. R., Clough, P. J., & Crust, L. (2015). Assessing model fit: Caveats and recommendations for confirmatory factor analysis and exploratory structural equation modeling. Measurement in Physical Education and Exercise Science, 19(1), 12-21.
https://doi.org/10.1080/1091367x.2014.952370
Rabaai, A. A. (2023). An investigation into the acceptance of mobile wallets in the FinTech era: An empirical study from Kuwait. International Journal of Business Information Systems, 44(4), 536-580. https://doi.org/10.1504/ijbis.2023.135350
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393-404.
https://doi.org/10.5465/amr.1998.926617
Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219-238.
Shanmugam, A., Savarimuthu, M. T., & Wen, T. C. (2014). Factors affecting Malaysian behavioral intention to use mobile banking with mediating effects of attitude. Academic Research International, 5(2), 236-253.
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.
https://doi.org/10.1016/j.jfoodeng.2005.02.010
Shin, D. (2018). Empathy and embodied experience in virtual environment: To what extent can virtual reality stimulate empathy and embodied experience. Computers in Human Behavior, 78, 64-73. https://doi.org/10.1016/j.chb.2017.09.012
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), 600-618. 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.
Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2014). Devising a research model to examine adoption of mobile payments: An extension of UTAUT2. The Marketing Review, 14(3), 310-335. https://doi.org/10.1362/146934714x14024779062036
Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147-169. https://doi.org/10.2307/248922
Sumsiripong, P. (2016). The impact of learning organization and competitive advantage on organizational performance in SMEs (Thailand): An empirical study [Doctoral Dissertation]. National Institute of Development Administration.
Teo, A. C., Tan, G. W. H., Ooi, K. B., & Lin, B. (2015). Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. International Journal of Mobile Communications, 13(5), 478-497. https://doi.org/10.1504/ijmc.2015.070961
Teo, T. S., Lim, V. K., & Lai, R. Y. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37. https://doi.org/10.1016/s0305-0483(98)00028-0
Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99-132.
https://doi.org/10.2753/mis0742-1222250303
Turban, E., King, D., Lee, J. K., Liang, T. P., & Turban, D. C. (2015). Electronic Commerce: A Managerial and Social Networks Perspective. Springer.
Ukut, I. I. T., & Krairit, D. (2019). Justifying students’ performance: A comparative study of both ICT students’ and instructors’ perspective. Interactive Technology and Smart Education, 16(1), 18-35.
Wang, C. S., Jeng, Y. L., & Huang, Y. M. (2017). What influences teachers to continue using cloud services? The role of facilitating conditions and social influence. The Electronic Library, 35(3), 520-533. https://doi.org/10.1108/el-02-2016-0046
Watjatrakul, B. (2013). Intention to use a free voluntary service: The effects of social influence, knowledge, and perceptions. Journal of Systems and Information Technology, 15(2), 202-220. https://doi.org/10.1108/13287261311328903
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. https://doi.org/10.1016/j.chb.2016.10.028
Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behavior & Information Technology, 33(10), 1027-1038.
https://doi.org/10.1080/0144929x.2014.934291
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
Yu, K., & Huang, G. (2020). Exploring consumers’ intent to use smart libraries with technology acceptance model. The Electronic Library, 38(3), 447-461. https://doi.org/10.1108/el-08-2019-0188
Zhou, T. (2011). Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207-218. https://doi.org/10.1177/0266666911414596
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