Assessment of Behavioral Intention to Use Tencent Meeting of First-Year Students for Legal Courses in Chengdu, China
Main Article Content
Abstract
Purpose: This research aims to assess the behavioral intention to use Tencent meetings of students for legal courses in Chengdu, China. The conceptual framework is developed from previous studies, incorporating perceived usefulness, attitude, social influence, perceived behavioral control, subjective norm, behavioral intention, and use behavior. Research design, data, and methodology: The target population is 500 first-year students at three selected universities who have experience using the Tencent platform for legal programs. The sample methods are judgmental, stratified random, and convenience sampling. Before the data collection, the Item Objective Congruence (IOC) Index and the pilot test (n=30) by Cronbach’s Alpha were assessed to ensure content validity and reliability. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used as statistical tools to confirm validity, reliability, and hypotheses testing. Results: The results show that all hypotheses are supported. Attitude, social influence, perceived behavioral control, and subjective norm significantly impacts behavioral intention and use behavior indirectly. Furthermore, perceived usefulness has a significant impact on attitude. Conclusions: The above key variables should be emphasized and strengthened to improve college students’ use behavior of Tencent meetings in the learning process. Universities ought to pay attention to enhancing a system to maximize students’ learning efficiency.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Ajzen, I. (1991). Theory of planned behavior. Organization Behavior and Human Decision Process, 50(2), 179-211.
Ajzen, I., & Fishbein, M. (2005). The Influence of Attitudes on Behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173–221). Lawrence Erlbaum Associates Publishers.
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273.
Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Universiti Teknologi Mara Kelantan.
Awwad, M. S., & Al-Majali, S. M. (2015). Electronic library services acceptance and use. The Electronic Library, 33(6),1100-1120. https://doi.org/10.1108/el-03-2014-0057
Bag, S., Aich, P., & Islam, M. A. (2022). Behavioral intention of “digital natives” toward adapting the online education system in higher education. Journal of Applied Research in Higher Education, 14(1), 16-40. https://doi.org/10.1108/JARHE-08-2020-0278.
Bajaj, A., & Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information & Management, 33(4), 213-224.
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
Besser, A., Flett, G. L., & Zeigler-Hill, V. (2022). Adaptability to a sudden transition to online learning during the COVID-19 pandemic: Understanding the challenges for students. Scholarship of Teaching and Learning in Psychology, 8(2), 85–105. https://doi.org/10.1037/stl0000198
Boateng, R., Mbrokoh, A. S., Boateng, L., Senyo, K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. The International Journal of Information and Learning Technology, 33(4), 248-262. https://doi.org/10.1108/ijilt-02-2016-0008
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press.
Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning: A combination of theory of reasoned action and technology acceptance model. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191. https://doi.org/10.1108/JRIT-03-2017-0004
Celik, H. (2016). Customer online shopping anxiety within the unified theory of acceptance and use technology (UTAUT) framework. Asia Pacific Journal of Marketing and Logistics, 28(2), 278-307. https://doi.org/10.1108/apjml-05-2015-0077
Chauhan, S., & Jaiswal, M. (2016). Determinants of acceptance of ERP software training in business schools: empirical investigation using UTAUT model. The International Journal of Management Education, 14(3), 248-262. https://doi.org/10.1016/j.ijme.2016.05.005
Chavan, M., & Carter, L. (2018). Management students – expectations and perceptions on work readiness. International Journal of Educational Management, 32(5), 825-850.
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.
De Haan, E., Kannan, P. K., 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
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
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1st ed.). Addison-Wesley. https://doi.org/10.2307/2065853
Foltz, C. B., Schwager, P. H., & Anderson, J. E. (2008). Why users (fail to) read computer usage policies. Industrial Management & Data Systems, 108(6), 701-712. https://doi.org/10.1108/02635570810883969
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Gable, R., & Wolf, M. (1993). Instrument Development in the Affective Domain: Measuring Attitudes and Values in Corporate and School Settings (2nd ed.). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-011-1400-4_1
Hou, H. (2015). What makes an online community of practice work? A situated study of Chinese student teachers’ perceptions of online professional learning. Teaching and Teacher Education, 46(2), 6-16. https://doi.org/10.1016/j.tate.2014.10.005
Hsiao, C.-H., & Tang, K.-Y. (2014). Explaining undergraduates’ behavior intention of e-textbook adoption: Empirical assessment of five theoretical models. Library Hi Tech, 32(1), 139-163. https://doi.org/10.1108/LHT-09-2013-0126
Hu, J., & Zhang, Y. (2016). Chinese students’ behavior intention to use mobile library apps and effects of education level and discipline. Library Hi Tech, 34(4), 639-656. https://doi.org/10.1108/lht-06-2016-0061
Huang, J., Lin, Y., & Chuang, S. (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
Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of Smartphone-Based Mobile Shopping: Mobile Benefits, Customer Characteristics, Perceived Risks, and the Impact of Application Context. Psychology and Marketing, 34(2), 175-194. https://doi.org/10.1002/mar.20982
Keong, M. L., Thurasamy, R., Sherah, K., & 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), 108-120. https://doi.org/10.1108/17515631211246249
Kim, Y. G., & Woo, E. (2016). Consumer acceptance of a quick response (QR) code for the food traceability system: Application of an extended technology acceptance model (TAM). Food Research International, 85, 266-272. https://doi.org/10.1016/j.foodres.2016.05.002
Lee, Y., Lee, J., & Lee, Z. (2006). Social influence on technology acceptance behavior: self-identity theory perspective. The Data Base for Advances in Information Systems, 37(2), 60-75. https://doi.org/10.1145/1161345.1161355
Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541.
Mytton, E., & Gale, C. (2012). Prevailing issues in legal education within management and business environments. International Journal of Law and Management, 54(4), 311-321.
Park, E. (2013). The adoption of tele-presence systems: Factors affecting intention to use tele-presence systems. Kybernetes, 42(6), 869-887. https://doi.org/10.1108/k-01-2013-0013
Pedersen, S., Cooley, P., & Cruickshank, V. (2017). Caution regarding exergames: a skill acquisition perspective. Physical Education and Sport Pedagogy, 22(3), 246-256. https://doi.org/10.1080/17408989.2016.1176131
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.
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 https://doi.org/10.1108/itse-11-2018-0092
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935-943. https://doi.org/10.1016/j.jbusres.2003.10.007
Shivdas, A., Menon, D. G., & Nair, C. S. (2020). Antecedents of acceptance and use of a digital library system: Experience from a Tier 3 Indian city. The Electronic Library, 38(1), 170-185. https://doi.org/10.1108/EL-03-2019-0074
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.
Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper. www.danielsoper.com/statcalc/default.aspx
Stangor, C. (2014). Research methods for the behavioral sciences (5th ed.). Cengage Learning.
Stockemer, D. (2019). Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in SPSS and Stata (1st ed.). Springer.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
Ukut, I. I. T., & Krairit, D. (2018). Justifying students’ performance, A comparative study of both ICT students’ and instructors’ perspective. Interactive Technology and Smart Education, 16(1), 18-35. https://doi.org/10.1108/itse-05-2018-0028
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. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1),156-178. https://doi.org/10.2307/41410412
Watjatrakul, B. (2016). Online learning adoption: effects of neuroticism, openness to experience, and perceived values. Interactive Technology and Smart Education, 13(3), 229-243. https://doi.org/10.1108/ITSE-06-2016-0017
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43, 728-739. http://dx.doi.org/10.1016/j.im.2006.05.002
Zhu, W., Wei, J., & Zhao, D. (2016). Anti-nuclear behavioral intentions: the role of perceived knowledge, information processing, and risk perception. Energy Policy, 88, 168-177. https://doi.org/10.1016/j.enpol.2015.10.009