The Adoption of the “Rain Classroom” Online Learning System among Sophomores in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2024.27Keywords:
Online Learning, Attitude, Effort Expectancy, Behavioral IntentionAbstract
Purpose: This study aims to examine the second-year students’ behavior intention and use behavior towards Rain Classroom online learning system in Chengdu, China. The major variables are used to develop a conceptual framework which are contains perceived usefulness, self-efficacy, attitude, subjective norms, effort expectancy, behavioral intention, and use behavior. Research design, data, and methodology: The quantitative study applied the questionnaire as a tool to collect the data from 500 students in selected three collages in Chengdu. The data were prior approved for content validity and constructs’ reliability in The Item-Objective Congruence (IOC) and pilot test (n=50) of Cronbach’s Alpha. The sampling techniques used are judgmental, stratified random, and convenience sampling. The data was analyzed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results: The results show most of hypotheses were supported, excepted the relationship between effort expectancy and use behavior. The support relationships are that perceived usefulness, self-efficacy and subjective norms significantly influence attitude. Additionally, self-efficacy, attitude, subjective norms, and effort expectancy significantly influence behavioral intention. Behavioral intention and use behavior are also significantly related. Conclusions: Rain Classroom can provide online learning to achieve a seamless connection between online and offline learning, which can be endorsed by the successful adoption of students in China.
References
Ajjan, H., Hartshorne, R., Cao, Y., & Rodriguez, M. (2014). Continuance use intention of enterprise instant messaging: A knowledge management perspective. Behaviour &Information Technology Archive, 33(7), 678-692.
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(6), 112-123.
https://doi.org/10.3991/ijet.v13i06.8275
Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Universiti Teknologi Mara Kelantan.
Bandura, A. (1997). Self-efficacy: The exercise of control (1st ed.). Worth Publishers.
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.
Bock, G.-W., Zmud, R., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Forces, and Organizational Climate. MIS Quarterly, 29(1), 87-111. https://doi.org/10.2307/25148669
Burton-Jones, A., & Hubona, G. S. (2005). Individual differences and usage behavior: Revisiting a technology acceptance model assumption. Data Base for Advances in Information Systems, 36(2), 58-77.
Calamlam, J. M. M. (2021). The Development of 21st-Century e-Learning Module Assessment Tool. Journal of Educational Technology Systems, 49(3), 289-309. https://doi.org/10.1177/0047239520953792
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
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
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.
Eastin, M. S. (2002). Diffusion of e-commerce: An analysis of the adoption of four e-commerce activities. Telematics and informatics, 19(3), 251-267.
Farah, M. F. (2017). Application of the Theory of Planned Behavior to Customer Switching Intentions in the Context of Bank Consolidations. International Journal of Bank Marketing, 35(1), 147-172. https://doi.org/10.1108/IJBM-01-2016-0003
Fatoni, A., Arifiati, N., Nurkhayati, E., Nurdiawati, E., Fidziah, A., Pamungkas, G., Adha, S., & Irawan, A., Purwanto, A., Julyanto, O., & Azizi, E. (2020). University students online learning system during Covid-19 pandemic: Advantages, constraints and solutions. Systematic Reviews in Pharmacy, 11(7), 570-576. https://doi.org/10.31838/srp.2020.7.81
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.
Gray, D. E. (2017). Doing research in the business world (1st ed.). Sage Publications.
Ha, H., & Janda, S. (2012). Predicting consumer intentions to purchase energy-efficient products. Journal of Consumer Marketing, 29(7), 461-469.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Han, L., & Lu, Z. (2020). Enhancing student participation in information literacy course based on Rain Classroom: A case study. Library Hi Tech, 38(3), 522-536.
Hoekstra, R., Bartels, M., Cath, D., & Boomsma, D. (2008). Factor structure, reliability and criterion validity of the Autism-Spectrum Quotient (AQ): A study in Dutch population and patient groups. Journal of Autism and Developmental Disorders, 38(8), 1555-1566.
Hong, S., & Yang, S.-U. (2009). Effects of Reputation, Relational Satisfaction, and Customer–Company Identification on Positive Word-of-Mouth Intentions. Journal of Public Relations Research, 21(4), 381-403. https://doi.org/10.1080/10627260902966433
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
Jaiyeoba, O. O., & Iloanya, J. (2019). E-learning in tertiary institutions in Botswana: apathy to adoption. International Journal of Information and Learning Technology, 36(2), 157-168. https://doi.org/10.1108/IJILT-05-2018-0058
Jugert, P., Greenaway, K., Barth, M., Büchner, R., Eisentraut, S., & Fritsche, I. (2016). Collective efficacy increases pro-environmental intentions through increasing self-efficacy. Journal of Environmental Psychology, 48, 12-23.
https://doi.org/10.1016/j.jenvp.2016.08.003
Kang, J., & Hustvedt, G. (2013). Building Trust Between Consumers and Corporations: The Role of Consumer Perceptions of Transparency and Social Responsibility. Journal of Business Ethics, 125(2), 1-10. https://doi.org/10.1007/s10551-013-1916-7
Lau, S.-H., & Woods, P. (2008). Understanding learner acceptance of learning objects: The roles of learning object characteristics and individual differences. British Journal of Educational Technology, 40(2), 1059-1075.
https://doi.org/10.1111/j.1467-8535.2008.00893.x
Lee, M., Rodgers, S., & Kim, M. (2009). Effects of Valence and Extremity of eWOM on Attitude toward the Brand and Website. Journal of Current Issues & Research in Advertising, 31(2), 1-11. https://doi.org/10.1080/10641734.2009.10505262
Lim, W. M., & Ting, D. H. (2014). Consumer Acceptance and Continuance of Online Group Buying. Journal of Computer Information Systems, 54(3), 87-96. https://doi.org/10.1080/08874417.2014.11645707
Maity, M., Bagchi, K., Shah, A., & Misra, A. (2018). Explaining normative behavior in information technology use. Information Technology & People, 32(1). 94-117.
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.
Ray, S., & Srivastava, S. (2020). Virtualization of science education: a lesson from the COVID-19 pandemic. Journal of proteins and proteomics, 11(2), 77-80. https://doi.org/10.1007/s42485-020-00038-7
Ruefenacht, S., Gebhardt-Henrich, S., Miyake, T., & Gaillard, C. (2002). A behaviour test on German Shepherd dogs: heritability of seven different traits. Applied Animal Behaviour Science, 79(2), 113-132.
https://doi.org/10.1016/S0168-1591(02)00134-X.
Schwartz, S. H. (1977). Normative Influence on Altruism. In L. Berkowitz (Ed.), Advances in Experimental Social Psychology (pp. 221-279). Academic Press. http://dx.doi.org/10.1016/s0065-2601(08)60358-5
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.
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.
Sutton, S. (1994). The past predicts the future: Interpreting behaviour–behaviour relationships in social psychological models of health behaviour. In D. R. Rutter & L. Quine (Eds.), Social psychology and health: European perspectives (pp. 71-88). Avebury/Ashgate Publishing Co.
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
Venkatesh, V., & Morris, M. G. (2000). Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24(1), 115-139. https://doi.org/10.2307/3250981
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.
Verhagen, T., Meents, S., & Tan, Y. H. (2006). Perceived risk and trust associated with purchasing at electronic marketplaces. European Journal of Information Systems, 15(6), 542-555.
Wang, J. (2020, May 16). Chengdu-Chongqing economic circle university alliance set up.
https://www.ichongqing.info/2020/05/16/chengdu-chongqing-economic-circle-university -alliance-set-up/
Westerbeek, H., & Shilbury, D. (2003). A Conceptual Model for Sport Services Marketing Research: Integrating Quality, Value and Satisfaction. International Journal of Sports Marketing and Sponsorship, 5(1), 11-31.
https://doi.org/10.1108/IJSMS-05-01-2003-B002
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
Yu, K., & Huang, G. (2019). Exploring consumers’ intent to use smart libraries with technology acceptance model. The Electronic Library, 38(3), 447-461.
Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model-a critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), 41-62.
