The Investigation on First-Year Students’ Use Behavior of Online Learning System or “Rain Classroom” in Chengdu, China
Article Sidebar
Main Article Content
Abstract
Purpose: “Rain classroom” is a smart teaching tool jointly developed by Xuetang and Tsinghua University Online Education Office. This study aims to investigate first-year students’ behavior intention and use behavior using the online learning system of Rain Classroom in Chengdu, China. The conceptual framework contains perceived usefulness, self-efficacy, attitude, subjective norms, effort expectancy, behavioral intention, and use behavior. Research design, data, and methodology: The study was conducted quantitatively by distributing questionnaires to 500 participants. The data were primarily tested for content validity and constructs’ reliability in The Item-Objective Congruence (IOC) and pilot test (n=50) of Cronbach’s Alpha. The sampling procedure involves judgmental, stratified random, and convenience sampling. The data was analyzed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results: The results show that perceived usefulness and subjective norms significantly influence attitude. Furthermore, self-efficacy, attitude, subjective norms, and effort expectancy significantly influence behavioral intention. Behavioral intention and use behavior are also significantly related. Nevertheless, non-support relationships exist between self-efficacy and attitude, and effort expectancy and use behavior. Conclusions: The application of rain classrooms can be enhanced by promoting its benefits and how such a system can optimize 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). The theory of planned behavior. Organizational and Human Decision Processes, 50(2), 179–211.
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.
Al-Maroof, R. A. S., & Al-Emran, M. (2018). Students Acceptance of Google Classroom: An Exploratory Study using PLS-SEM Approach. International Journal of Emerging Technologies in Learning (iJET), 13(06), 112–123. https://doi.org/10.3991/ijet.v13i06.8275
Anderson, C. L., & Agarwal, R. (2010). Practicing Safe Computing: A Multimedia Empirical Examination of Home Computer User Security Behavioral Intentions. Quarterly 34(3), 613-643.
Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Universiti Teknologi Mara Kelantan.
Beatty, B., & Ulasewicz, C. (2006). Faculty perspectives on moving from blackboard to the module learning management system. Research and Practice to Improve Learning, 50(4), 36-45.
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), 351–370.
Cao, Y., & Jittawiriyanukoon, C. (2022). Factors Impacting Online Learning Usage during Covid-19 Pandemic Among Sophomores in Sichuan Private Universities. AU-GSB E-JOURNAL, 15(1), 152-163. https://doi.org/10.14456/augsbejr.2022.52
Chadda, I., & Kaur, H. (2021). COVID pandemic and virtual classes: a study of students from Punjab. Asian Association of Open Universities Journal, 16(2), 193-210. https://doi.org/10.1108/AAOUJ-02-2021-0017
Chiu, C.-M., Chang, C.-C., Cheng, H.-L., & Fang, Y.-H. (2009). Determinants of customer repurchase intention in online shopping. Online Information Review, 33(4), 761-784. https://doi.org/10.1108/14684520910985710
Constant, D., Kiesler, S., & Sproull, L. (1994). What's Mine Is Ours, or Is It? A Study of Attitudes about Information Sharing. Information Systems Research, 5(4), 400-421. https://doi.org/10.1287/isre.5.4.400
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Dowling, M. (2007). From Husserl to Van Manen: A review of different phenomenological approaches. International journal of nursing studies, 44(1), 131-42. https://doi.org/10.1016/j.ijnurstu.2005.11.026
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research (1st ed.). Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18, 382-388. http://dx.doi.org/10.2307/3150980
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.
Groening, C., Sarkis, J., & Zhu, Q. (2017). Green Marketing Consumer-Level Theory Review: A Compendium of Applied Theories and Further Research Directions. Journal of Cleaner Production, 172(10), 1848-1866. https://doi.org/1016/j.jclepro.2017.12.002
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 1–15). Sage Publications, Inc.
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
Kiran, R., Sharma, A., & Mittal, K. C. (2008). Attitudes, Preferences and Profile of Online Buyers in India: Changing Trends. South Asian Journal of Management, 15(3), 56-73.
Lee, H., & Jin, M. Y. (2012). Consumer perceptions of online consumer product and service reviews: Focusing on information processing confidence and susceptibility to peer influence. Journal of Research in Interactive Marketing, 6(2), 110–132.
Maity, M., Bagchi, K., Shah, A., & Misra, A. (2018). Explaining normative behavior in information technology use. Information Technology & People, 32(1). 94-117.
Nimako, S., Gyamfi, N. K., & Wandaogou, A. (2013). Effect of Internet Banking Service Quality on Customer Satisfaction: Evidence from Ghanaian Banking Industry. Research Journal of Social Science and Management, 3(4), 1-14.
Park, E. (2013). The adoption of tele-presence systems: Factors affecting intention to use tele-presence systems. Kybernetes, 42(6), 869–887.
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.
Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194–198. https://doi.org/10.1037/a0030767
Ross, S. E., Niebling, B. C., & Heckett, T. M. (1999). Sources of stress among college students. College Student Journal, 33(1), 316-318.
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.
Shim, S., Eastlick, M., Lotz, S., & Warrington, P. (2001). An Online Prepurchase Intentions Model: The Role of Intention to Search. Journal of Retailing - J RETAIL, 77(3), 397-416. https://doi.org/10.1016/S0022-4359(01)00051-3
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.
Soper, D. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper. https://www.danielsoper.com/statcalc
Stangor, C. (2014). Research methods for the behavioral sciences (5th ed.). Cengage Learning.
Tang, M., Addison, K. D., Lasure-Bryant, D., Norman, R., O’Connell, W., & Stewart-Sicking, J. A. (2004). Factors that influence self-efficacy of counseling students: An exploratory study. Counselor Education and Supervision, 44(1), 70–80.
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
Tarkiainen, A., & Sundqvist, S. (2005). Subjective norms, attitudes and intentions of Finnish consumers in buying organic food. British Food Journal, 107(11), 808-822. https://doi.org/10.1108/00070700510629760
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440.
Troudi, H., & Bouyoucef, D. (2020). Predicting purchasing behavior of green food in Algerian context. EuroMed Journal of Business, 15(1), 1-21. https://doi.org/10.1108/EMJB-03-2019-0046
Vazifehdoust, H., Taleghani, M., Esmaeilpour, F., Nazari, K., & Khaudang, M. (2013). Purchasing green to become greener: Factors influence consumers’ green purchasing behavior. Management Science Letters, 3(9), 2489–2500.
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., & 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.
Woon, I. M. Y., Tan, G. W., & Low, R. T. (2005). A protection motivation theory approach to home wireless security. Proceedings of the 26th International Conference on Information Systems, 367–380. https://aisel.aisnet.org/icis2005/31
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. https://doi.org/10.1016/j.im.2006.05.002
Wu, Y., Wu, T., & Li, Y. (2019). Impact of using classroom response systems on students’ entrepreneurship learning experience. Computers in Human Behavior, 92(1), 634-645. https://doi.org/10.1016/j.chb.2017.08.013
Yu, K., & Huang, G. (2019). Exploring consumers’ intent to use smart libraries with technology acceptance model. The Electronic Library, 38(3), 447-461.