INFLUENCING FACTORS OF BEHAVIORAL INTENTION AND SATISFACTION OF ONLINE LEARNING AMONG UNDERGRADUTES IN CHENGDU UNIVERSITY OF CHINA

Authors

  • Wencai Lan Ph.D. Candidate, Doctor of Philosophy, Technology Education Management, Assumption University
  • Chaochu Xiang Academy of Arts and Design, Chengdu University of China
  • Deping Feng Dean of the Department of Marxism and Fundamental Education, Chongqing Vocational College of Intelligent Engineering, China

Keywords:

Undergraduate, Online Learning, Behavioral Intention, Satisfaction, Technology Adoption Model

Abstract

The objective of this research is to investigate the behavioral intention and satisfaction of students towards online learning, in order to provide corresponding theoretical support for future planning and implementation of teaching reform. Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Information Systems Success (ISS) were applied to build a research model, consisted of perceived ease of use, perceived usefulness, self-efficacy, effort expectancy, social influence, behavioral intentions, and satisfaction. The sampling techniques used were judgmental sampling, stratified random sampling, and convenience sampling. The target population and sample size were collected by distributing online questionnaire to 500 undergraduate students, majoring in economics, physical science, art design, and bioengineering in Chengdu university of China. Item- Objective Congruence (IOC) for the content validity and Cronbach's Alpha for reliability analysis were conducted before processing of the data collection. Afterwards, the descriptive analysis, Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were accounted. The results showed a significant relationship between perceived ease of use and perceived usefulness towards behavioral intention, and social influence on behavioral intention towards satisfaction. For non-significant relationships, there were self-efficacy, perceived ease of use and effort expectancy on behavioral intention. Academic researchers and education’s stakeholders should extend the study to ensure the high-quality online learning system for increasing the level of behavioral intention and satisfaction of the system.

References

Abbad, M. M. (2013). e-Banking in Jordan, Behavior and Information Technology, 32(7), 618-694.

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.

Alam, M. S., & Uddin, M. A. (2019). Adoption and Implementation of Enterprise Resource Planning (ERP): An Empirical Study. Journal of Management and Research (JMR), 6(1), 84-116.

Al-Azawei, A., & Lundqvist, K. (2015). Learner Differences in Perceived Satisfaction of an Online Learning: An Extension to the Technology Acceptance Model in an Arabic Sample, Learner Differences in Perceived Satisfaction of an Online Learning: An Extension to the Technology Acceptance Model in an Arabic Sample. The Electronic Journal of e-Learning, 13(5), 408-426.

Aristovnik, A., Keržič. D., Ravšelj. D., Tomaževič. N., & Umek, L. (2020). Impacts of the COVID-19 Pandemic on Life of Higher Education Students: A Global Perspective. Sustainability, 12(20), 8438.

Bandura, A. (1982). Self-Efficacy Mechanism in Human Agency. American Psychologist, 37(2), 122-147.

Bardakc, S. (2019). Exploring High School Students' Educational Use of YouTube. International Review of Research in Open and Distributed Learning, 20(2), 260-278.

Barrane, F. Z., Karuranga, G. E, & Poulin, D. (2018). Technology Adoption and Diffusion: A New Application of the UTAUT Model. International Journal Innovative. Technology Management, 15(6), 1950004–1950023.

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

Çelik, H. (2008). What Determines Turkish Consumers’ Acceptance of Internet Banking? International Journal of Bank Marketing, 26(5), 353-370.

Chao, C. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology, 10, https://doi.org/10.3389/fpsyg.2019.01652

Chen, Q., & Bao, H. (2014). The Development and Current situation of Chinese Online Education, Higher Education Press.

Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An Investigation of Mobile Learning Readiness in Higher Education Based on The Theory of Planned Behavior. Computers & Education, 59(3), 1054-1064.

Cheung, R., & Vogel, D. (2013). Predicting User Acceptance of Collaborative Technologies: An Extension of The Technology Acceptance Model for e-Learning. Computers & Education, 63(1), 160-175.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-339.

Davis, F. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions, and Behavioral Impacts. International Journal of Man-Machine Studies, 38, 475-487.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982-1003.

DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60-95.

DeLone, W. H., & McLean, E. R. (2002). Information Systems Success Revisited. Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS), Big Island, Hawaii, 238-249.

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719–734.

Fan, H. (2017). The Present Situation and Countermeasures of Educational Equity in China. Academy, 21(1), 115-116.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.

Guan, J., & Li, Q. T. (2017). Chinese Online Education Current Situation, Trend and Experience for Reference. China Educational Technology, 331(1), 62-66.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.

Henry, J. W., & Stone, R. W. (1995). A Structural Equation Model of Job Performance Using a Computer-Based Order Entry System. Behavior and Information Technology, 14(3), 163-173.

Jiang, S. (2021). The Development Course and New Path of Online Education of Education Association. Editors Monthly, 102-106.

Kesharwani, A., & Tripathy, T. (2012), Dimensionality of Perceived Risk and Its Impact on Internet Banking Adoption: An Empirical Investigation. Services Marketing Quarterly, 33(2), 177-193.

Lee, M. C. (2009), Factors Influencing the Adoption of Internet Banking: An Integration of TAM and TPB With Perceived Risk and Perceived Benefit. Electronic Commerce Research and Applications, 8(3). 130-141.

Liaw, S. S., & Huang, H. M. (2003). An Investigation of User Attitudes Toward Search Engines as An Information Retrieval Tool. Computers In Human Behavior, 19(6), 751–765. https://doi.org/10.1016/S0747-5632(03)00009-8

Lin, J. C., &, Hsieh, P. (2006). The Role of Technology Readiness in Customers’ Perception and Adoption of Self-Service Technologies. International Journal of Service Industry Management, 179(5), 497-517.

Maphosa, V., Dube, B., & Jita, T. (2020). A UTAUT Evaluation of WhatsApp as a Tool for Lecture Delivery During the COVID-19 Lockdown at a Zimbabwean University. International Journal of Higher Education, 9(5), 84-93.

Nagy, J. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Mode. International Review of Research in Open and Distributed Learning, 19(1), 160-185.

Nunnally, J. C., & Bernstein, I. R. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Oliver, R. L. (1993). Cognitive, Affective, and Attribute Bases of the Satisfaction Response, Journal of Consumer Behavior, 20(3), 418-430.

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.

Pozón-López, I., Kalinic, Z., Higueras-Castillo, E., & Liébana-Cabanillasa. (2020). A Multi-Analytical Approach to Modeling of Customer Satisfaction and Intention to Use in Massive Open Online Courses (MOOC). Interactive Learning Environments, 28(8), 1003-1021.

Rajan, C., & Baral, R. (2015). Adoption of ERP System: An Empirical Study of Factors Influencing the Usage of ERP and its Impact on End User. IIMB Management Review, 27(2), 105-117. http://doi.org/10.1016/j.iimb.2015.04.008

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.

Shin, W. S., & Kang, M. (2015). The Use of a Mobile Learning Management System at an Online University and Its Effect on Learning Satisfaction and Achievement. International Review of Research in Open and Distributed Learning, 16(3), 110-130.

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. (2022). A-priori Sample Size Calculator for Structural Equation Models [Software]. www.danielsoper.com/statcalc/default.aspx

Teo, T. (2009). Modelling Technology Acceptance in Education: A Study of Pre-Service Teachers. Computers & Education, 52(2), 302-312.

Teo, T., & Noyes, J. (2014). Explaining the Intention to Use Technology among Pre-Service Teachers: A Multi-Group Analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51-66.

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of The Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.

Venkatesh, V., Morris, M. G., Hall, M., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). User Acceptance of Information Technology: Toward A Unified View 1. MIS Quarterly, 27(3), 425-478.

Vululleh, P. (2018). Determinants Of Students’ E-Learning Acceptance in Developing Countries: An Approach Based on Structural Equation Modeling (SEM). International Journal of Education and Development Using Information and Communication Technology, 14(1), 141-151.

Wu, J., & Wang, S. (2005). What Drives Mobile Commerce? An Empirical Evaluation of the Revised Technology Acceptance Model. Information & Management, 42, 719-729. http://doi.org/10.1016/j.im.2004.07.001

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

Yi, M. Y., & Hwang, Y. (2003). Predicting The Use of Web-Based Information Systems: Self-Efficacy, Enjoyment, Learning Goal Orientation, And the Technology Acceptance Model. International Journal of Human-Computer Studies, 59(4), 431-449.

Yunus, A., & Mohammad, A. (2017). A Proposed Framework based Electronic Medical Records (ERM) for Implementation of Technology Acceptance in Healthcare Service. International Journal of Academic Research in Business and Social Sciences, 7(9), 96-115. http://doi.org/10.6007/IJARBSS/v7-i9/331

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Published

2022-12-29

Issue

Vol. 8 No. 2 (2022): July-December, 2022

Section

Research Articles

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