DETERMINANTS OF STUDENTS’ BEHAVIORAL INTENTION TO USE MOBILE LEARNING DURING COVID-19 IN CHENGDU, CHINA
Keywords:
Mobile Learning, Higher Education, Behavioral Intention, Covid-19, UTAUTAbstract
The purpose of this research is to examine the determinants of undergraduate students’ behavioral intention to use mobile learning (M-learning) during Covid-19 pandemic. The conceptual framework was based on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The sampling techniques used were judgmental sampling, quota sampling, and convenience sampling. The target population and sample size were collected by the questionnaire distribution to 500 respondents who are undergraduate students in the three selected universities in Chengdu, China. Before the data collection, Item- Objective Congruence (IOC) was tested for the content validity and Cronbach's Alpha was performed for an inter-item reliability analysis. After collecting the data, demographic information was summarized by descriptive analysis. Confirmatory Factor Analysis (CFA) was applied to test validity and reliability of measurement model. Lastly, Structural Equation Model (SEM) was accounted to measure structural model and hypothesis testing. As a results, there was a significant relationship between perceived usefulness and attitude toward behavioral intention. Effort expectancy, self-efficacy and facilitating condition significantly impacted behavioral intention. Nevertheless, attitude and social influence had no significant impact on behavioral intention. The recommendations for academic practitioners and school management team were to design user’s friendly function and promote benefits of a system to build positive attitude and behavior intention to use mobile learning.
References
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall.
Al-Hujran, O., Al-Lozi, E., & Al-Debei, M. M. (2014). Get ready to mobile learning”: Examining factors affecting college students’ behavioral intentions to use m-learning in Saudi Arabia. Jordan Journal of Business Administration, 10(1), 1-18. https://doi.org/10.12816/0026186
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.
Andoh, B.(2018). Predicting students’ intention to adopt mobile learning. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191.
Bajaj, A., & Nidumolu, S. R. (1998) A Feedback Model to Understand Information System Usage. Information & Management, 33, 213-224. https://doi.org/10.1016/S0378-7206(98)00026-3
Burns, N., & Grove, S. K. (1997). The Practice of Nursing Research: Conduct, Critique and Utilisation (3rd ed.). Saunders.
Castañeda, A., Ríos, F., & Luque Martínez, T. (2007). The dimensionality of customer privacy concern on the internet. Online Information Review, 31(4), 420-439. https://doi.org/10.1108/14684520710780395
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. https://doi.org/10.1287/mnsc.35.8.982.
Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of b2C channel satisfaction and preference: validating e-commerce metrics, Information Systems Research, 13(3), 316-333.
Doll, W. J., Hendrickson, A. & Deng, X. (1998). Using Davis’s perceived usefulness and ease-of-use instruments for decision making: a confirmatory and multi-group invariance analysis, Decision Sciences, 29(4), 839-69.
Dwivedi, K., Rana, P., Chen, H., & Williams, D. (2011). A meta-analysis of the unified theory of acceptance and use of technology (UTAUT). Proceedings of the International Conference on Governance and Sustainability in Information Systems.
Eom, S. B. (2012). Effects of LMS, self-efficacy, and self-regulated learning on LMS effectiveness in business education. Journal of International Education in Business, 5(2), 129-144. https://doi.org/10.1108/18363261211281744
Fidani, A., & Idrizi, F. (2012). Investigating students’ acceptance of a learning management system in university education: a structural equation modeling approach. ICT Innovations, 311.
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
Forza, C. & Filippini, R. (1998) TQM impact on quality conformance and customer satisfaction: a causal model. International Journal of Production Economics, 55(1), 1-20.
Ghazali, E. M., Mutum, D. S., Chong, J .H., & Nguyen, B. (2018). Do consumers want mobile commerce? A closer look at M-shopping and technology adoption in Malaysia. Asia Pacific Journal of Marketing and Logistics, 30(4), 1064-1086.
Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340-2350.
Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: an empirical study. Journal of Strategic Information Systems, 17(2), 140‐154.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.
King, W., & He, J. (2006). A meta-analysis of the Technology Acceptance Model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). The Guilford Press.
Lee, L., Petter, S., Fayard, D., & Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305-328.
https://doi.org/10.1016/j.accinf.2011.05.002
Li, A. Q. (2020). Research on the development of college students' mobile learning power. Dalian University of Technology press.
Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57(7), 738-756.
Mathwick, C., Malhotra, N., & Rigdon, E. (2001). Experiential value: conceptualization, measurement and application in the catalog and Internet shopping environment. Journal of Retailing, 77(1), 39-56.
Mungania, P., & Reio, T. G. (2005, February 24-27). If e-learners get there, will they stay? the role of e-learning self-efficacy [Paper Presentation]. Academy of Human Resource Development International Conference (AHRD), Estes Park, CO. http://www.eric.ed.gov/PDFS/ED492287.pdf
Nunnally, J. C., & Bernstein, I. H. (1994) The Assessment of Reliability. Psychometric Theory, 3, 248-292.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor anlaysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.
Raman, A., & Don, Y. (2013). Preservice Teachers’ Acceptance of Learning Management Software: An Application of the UTAUT2 Model. International Education Studies, 6(7), https://doi.org/10.5539/ies.v6n7p157
Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems. Interactive Technology and Smart Education, 16(3), 219-238.
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. S. (n.d.). A-priori Sample Size Calculator for Structural Equation Models [Software]. www.danielsoper.com/statcalc/default.aspx
Studenmund, A. H. (1992). Using Econometrics: A Practical Guide. Harper Collins.
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),
-182. https://doi.org/10.1108/JIEB-09-2016-0032
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.
Wei, M. (2018). Research on influencing factors model of college students' willingness to use mobile learning. Minzu university of China.
Yadav, R., Sharma, S.K., & Tarhini, A. (2016). A multi-analytical approach to understand and predict the mobile commerce adoption. Journal of Enterprise Information Management, 29(2), 222-237.
Zhang, S., Zhao, J., & Tan, W. (2008) Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science & Technology, 13(3), 312-317.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Suvarnabhumi Institute of Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของวารสารวิชาการ สถาบันเทคโนโลยีแห่งสุวรรณภูมิ
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับสถาบันเทคโนโลยีแห่งสุวรรณภูมิ และคณาจารย์ท่านอื่นๆในสถาบันฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว
2.jpg)
