How Do Undergraduate Students Adopt Online Learning in Chengdu, China During COVID-19?
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Abstract
Purpose: This study aims to examine the online learning adoption of college students in Chengdu, China. The main variables constructed in a conceptual framework based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) are perceived ease of use, perceived usefulness, attitude, social influence, facilitating conditions, behavioral intention, and user behavior. Research design, data, and methodology: The target population is 500 undergraduates. The sample techniques are purposive, stratified random, convenience, and snowball samplings. Before collecting the data, The Item Objective Congruence (IOC) Index and the pilot test (n=50) by Cronbach’s Alpha were used to assure content validity and construct validity. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results: The results reveal that perceived ease of use significantly impacts perceived usefulness and behavioral intention. Attitude and facilitating conditions significantly impact behavioral intention. Behavioral intention has a significant impact on user behavior. On the contrary, perceived usefulness and social influence have no significant impact on behavioral intention. Conclusions: To ensure that all students can adopt digital learning successfully, educational institutions and the Chinese government needs to improve accessibility with the highest-performance online learning infrastructure for the country.
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References
Abbad, M. M., Morris, D., & De Nahlik, C. (2009). Looking under the bonnet: Factors affecting student adoption of e-learning systems in Jordan. International Review of Research in Open and Distributed Learning, 10(2), 1-23.
Ajzen, I. (2005). Attitudes, Personality and Behaviour (2nd ed.). Open University Press.
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.
Balacheff, N., & Kaput, J. J. (1996). Computer-based learning environments in mathematics. In A. Bischop (Ed.), International Handbook of Mathematics Education (pp. 469–501). Kluwer Academic publisher.
Baum, S., & McPherson, M. (2019). The human factor: the promise & limits of online education. Daedalus, 148(4), 235–254.
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
Bervell, B., Nyagorme, P., & Arkorful, V. (2017). LMS-enabled hybrid learning use intentions among distance education tutors: Examining the mediation role of attitude based on technology-related stimulus-response theoretical framework. Contemporary Educational Technology, 12(2), 1-21.
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
Chauhan, S. (2015). Acceptance of mobile money by poor citizens of India: integrating trust into the technology acceptance model. Info, 17(3), 58-68.
Chiu, J. L., Bool, N. C., & Chiu, C. L. (2017). Challenges and factors influencing initial trust and behavioral intention to use mobile banking services in the Philippines. Asia Pacific Journal of Innovation and Entrepreneurship, 11(2), 246-278. https://doi.org/10.1108/APJIE-08-2017-029
Chuttur, M. (2009). Overview of the technology acceptance model: Origins, development and future directions. Sprouts: Working Papers on Information Systems, 9(37), 1-21.
Cohen, J. A. (2022). Considerations associated with synchronous and asynchronous video use in online learning. Development and Learning in Organizations, 36(6), 1-3. https://doi.org/10.1108/DLO-10-2021-0185
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 19-340.
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.
Dhawan, S. (2020). Online learning: a panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22.
Efiloğlu Kurt, Ö. (2019). Examining an e-learning system through the lens of the information systems success model: Empirical evidence from Italy. Education and Information Technologies, 24(2), 1173–1184. https://doi.org/10.1007/s10639-018-9821-4
Fishbein, M., & Ajzen, I. (1981). Attitudes and voting behavior: An application of the theory of reasoned action. In G. M. Stephenson & J. M. Davis (Eds.), Progress in Applied Social Psychology (pp. 253-313). Wiley.
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
Griethuijsen, R. A. L. F., Eijck, M. W., Haste, H., Brok, P. J., Skinner, N. C., Mansour, N., Gencer, A. S., & BouJaoude, S. (2014). Global patterns in students’ views of science and interest in science. Research in Science Education, 45(4), 581–603. https://doi.org/10.1007/s11165-014-9438-6
Guo, C., & Wan, B. (2022). The digital divide in online learning in China during the COVID-19 pandemic. Technology in society, 71, 102-122. https://doi.org/10.1016/j.techsoc.2022.102122
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson.
Hambleton, R. K., Swaminathan, H., Algina, J., & Coulson, D. B. (1978). Criterion-referenced testing and measurement: A review of technical issues and developments. Review of Educational Research, 48(1), 1-47. https://doi.org/10.2307/1169908
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
Kanwal, F., Kramer, J., Asch, S. M., El-Serag, H., Spiegel, B. M., Edmundowicz, S., Sanyal, A. J., Dominitz, J. A., McQuaid, K. R., Martin, P., Keeffe, E. B., Friedman, L. S., Ho, S. B., Durazo, F., & Bacon, B. R. (2010). An explicit quality indicator set for measurement of quality of care in patients with cirrhosis. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 8(8), 709–717. https://doi.org/10.1016/j.cgh.2010.03.028
Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89.
Leaderer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269-282.
Lin, H. (2013). The effect of absorptive capacity perceptions on the context‐aware ubiquitous learning acceptance. Campus-Wide Information Systems, 30(4), 249-265. https://doi.org/10.1108/CWIS-09-2012-0031
Luo, L., Pibulcharoensit, S., Kitcharoen, K., & Feng, D. (2022). Exploring Behavioral Intention Towards Hybrid Education of Undergraduate Students in Public Universities in Chongqing, China. AU-GSB E-JOURNAL, 15(2), 178-186. https://doi.org/10.14456/augsbejr.2022.83
Lynch, K., & Kim, J. S. (2017). Effects of a summer mathematics intervention for low-income children: a randomized experiment. Education Policy Analysis Archives, 39(1), 31–53.
Martins, C., Oliveira, T., & Popovic, A. (2014). Understanding the Internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13.
McBurnie, C., Adam, T., & Kaye, T. (2020). Is there learning continuity during the COVID-19 pandemic? A synthesis of the emerging evidence. Journal of Learning for Development, 7(3), 485–493.
McDonald, R. P., & Ho, M. H. (2002). Principles and practice in reporting structural equation analyses. Psychol. Methods, 7, 64–82.
Min, Y., Huang, J., Varghese, M. M., & Jaruwanakul, T. (2022). Analysis of Factors Affecting Art Major Students’ Behavioral Intention of Online Education in Public Universities in Chengdu. AU-GSB E-JOURNAL, 15(2), 150-158. https://doi.org/10.14456/augsbejr.2022.80
Organisation for Economic Co-operation and Development. (2021). The State of Global Education: 18 Months into the pandemic. OECD Publishing. https://doi.org/10.1787/1a23bb23-en
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.
Queiroz, M. M., & Wamba, F. S. (2019). Blockchain adoption challenges in supply chain: an empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82.
Reimers, F. M., & Schleicher, A. (2020, March 30). A framework to guide an education response to the COVID-19 pandemic of 2020. OECD Publishing. https://doi.org/10.1787/6ae21003-en
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
Shen, C. W., Ho, J. T., & Pham, L., & Kuo, T. C. (2019). Behavioural intentions of using virtual reality in learning: perspectives of acceptance of information technology and learning style. Virtual Reality, 23(3), 129-137. https://doi.org/10.1007/s10055-018-0348-1
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
Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13, 380-427.
Teo, H. H., Chan, H. C., Wei, K. K., & Zhang, Z. (2003). Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities. International Journal of Human-Computer Studies, 59(5), 671-697.
Tran, L. T. T., & Nguyen, P. T. (2021). Co-creating blockchain adoption: theory, practice and impact on usage behavior. Asia Pacific Journal of Marketing and Logistics, 33(7), 1667-1684. https://doi.org/10.1108/APJML-08-2020-0609
Turner, R., & Carlson, L. (2003). Indexes of Item-Objective Congruence for Multidimensional Items. International Journal of Testing, 3(2), 163-171. https://doi.org/10.1207/S15327574IJT0302_5
UNESCO. (2022, April 21). How is China ensuring learning when classes are disrupted by coronavirus?. https://www.unesco.org/en/articles/how-china-ensuring-learning-when-classes-are-disrupted-coronavirus
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 45(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, F. D., & Davis, G. B. (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.
Vermeir, I., & Verbeke, W. (2006). Sustainable Food Consumption: Exploring the Consumer “Attitude–Behavioral Intention” Gap. Journal of Agricultural and Environmental Ethics, 19(2), 169-194.
Wongsabsin, S. (2021). Determinants Of Enterprise Resource Planning (ERP) Systems Adoption in Thai Private Companies. Journal of Management and Marketing, 8(2), 118-135.
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
Xie, H., Kitcharoen, K., Leelakasemsant, C., & Varghese, M. M. (2022). The Effect of Behavioral Intention to Use Hybrid Education: A Case of Chinese Undergraduate Students. AU-GSB E-JOURNAL, 15(2), 159-168. https://doi.org/10.14456/augsbejr.2022.81
Zhong, K., Feng, D., Yang, M., & Jaruwanakul, T. (2022). Determinants of Attitude, Satisfaction and Behavioral Intention of Online Learning Usage Among Students During COVID-19. AU-GSB E-JOURNAL, 15(2), 49-57. https://doi.org/10.14456/augsbejr.2022.71