Determinants of Attitude, Satisfaction and Behavioral Intention of Online Learning Usage Among Students During COVID-19

Main Article Content

Kexun Zhong
Deping Feng
Ming Yang
Thanatchaporn Jaruwanakul

Abstract

Purpose: The objective of this research is to examine determinants of behavioral intention to use online learning among students in a higher vocational collage in China, including perceived ease of use, perceived usefulness, attitude, trust and satisfaction. Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) were the fundamental theories of this study. Research design, data and methodology: A quantitative approach was used to distribute questionnaire to 500 third-grade students in Tianfu Vocational College of Chengdu. Purposive sampling, stratified random sampling, and convenience sampling were employed. After the data collection, confirmatory factor analysis (CFA) and structural equation model (SEM) were accounted to analyze the data in measurement and structural models, measuring factor loading, reliability, validity and model fit. Results: Perceived ease of use had the strongest influence on perceived usefulness, followed by perceived ease of use on attitude, attitude on behavioral intention, and perceived usefulness on behavioral intention. In opposite, the non-supported relationships were perceived usefulness and attitude, trust and satisfaction, and satisfaction and behavioral intention. Conclusions: The findings implied that academic researchers and education’s stakeholders should emphasize ease-of-use and benefits of using an online learning system that can help students’ learning experience to be more conveniently and effectively.

Downloads

Download data is not yet available.

Article Details

How to Cite
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
Section
Articles
Author Biographies

Kexun Zhong

Ph.D. Candidate, Doctor of Philosophy, Technology Education and Management, Assumption University, Thailand.

Deping Feng

Dean, Department of Marxism and Fundamental Education, Chongqing Vocational College of Intelligent Engineering, China.

Ming Yang

Department of Animation, School of Film Television and Animation, Chengdu University, China.

Thanatchaporn Jaruwanakul

Associate Director, Strategic Policy Development, True Corporation Public Company Limited.

References

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-224. https://doi.org/10.1287/isre.9.2.204.

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

Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall.

Athiyaman, A. (1997). Linking student satisfaction and service quality perceptions: the case of university education. European Journal of Marketing, 31(7), 528-540.

Bashir, I., & Madhavaiah, C. (2015). Consumer Attitude and Behavioral Intention Towards Internet Banking Adoption in India. Journal of Indian Business Research, 7(1), 67-102.

Browne, B. A., Kaldenberg, D., Browne, W. G., & Brown, D. J. (1998). Student as customer: Factors affecting satisfaction and assessments of institutional quality. Journal of Marketing for Higher Education 8(3), 1-14.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen, K. A. & Long, J. S. (Eds.). Testing structural equation models (pp.136-162). SAGE Publication.

Carlson, J., & O’Cass, A. (2010). Exploring the Relationships between E-Service Quality, Satisfaction, Attitudes and Behaviours in Content-Driven E-Service Web Sites. Journal of Services Marketing, 24, 112-127.

http://dx.doi.org/10.1108/08876041011031091

Charness, N., & Boot, W. (2009). Aging and Information Technology Use: Potential and Barriers. Current Directions in Psychological Science, 18(5). 253-258.

https://doi.org/10.1111/j.1467-8721.2009.01647.x

Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: the role of brand loyalty. Journal of Marketing, 65(2), 81-93.

Chauhan, S. (2015). Acceptance of mobile money by poor citizens of India: integrating trust into the technology acceptance model. info, 17(3), 58-68.

Chen, Y. C. (2017). The relationships between brand association, trust, commitment, and satisfaction of higher education institutions. International Journal of Educational Management, 31(7), 973-985. https://doi.org/10.1108/IJEM-10-2016-0212

Chiou, J. S., & Shen, C. C. (2012). The Antecedents of Online Financial Service Acceptance: The Impact of Physical Banking Services on Internet Banking Acceptance. Behavior and Information Technology, 31(9), 859-871.

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

Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120-123.

Clemes, M. D., Gan, C., & Kao, T. H. (2008). University Student Satisfaction: An Empirical Analysis. Journal of Marketing for Higher Education 17(2), 292-325.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.1177/0022429411405207.

Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3),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(8), 982-1003.

Delgado-Ballester, E., & Luis Munuera-Alemán, J. (2001). Brand trust in the context of consumer loyalty. European Journal of Marketing, 35(11/12),1238-1258.

Fishbein, M., & Ajzen, 1. (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. (1992). A national customer satisfaction barometer: the Swedish experience. Journal of Marketing, 56(1),6-21.

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

Gray, D., & Daymond, J. (2010). The Influence of student engagement levels on satisfaction and behavioral intentions [Paper Presentation]. Australian & New Zealand Marketing Academy Conference, Christchurch, New Zealand.

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

Kotler, P. (2000). Marketing Management: Analysis, Planning, Implementation, and Control (10th ed.). Prentice Hall.

Lee, L. O., Aldwin, C. M., Kubzansky, L. D., Chen, E.,

Mroczek, D. K., Wang, J. M., & Spiro, A. (2015). Do

cherished children age successfully? Longitudinal findingsfrom the Veterans Affairs Normative Aging Study. Psychology and aging, 30(4), 894.

Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: a test of the theory of reasoned action. Journal of Engineering & Technology Management, 14(2), 147-173.

Machleit, K., & Mantel, S. (2001). Emotional response and shopping satisfaction: moderating effects of shopper attributions. Journal of Business Research, 54(2), 97-106.

Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information System Researc, 2(3), 173-191.

Moorman, C., Deshpande, R., & Zaltman, G. (1993). Factors affecting trust in market research relationships. Journal of Marketing. 57(1), 81-101.

Mouakket, S., & Bettayeb, A. M. (2015). "Investigating the factors influencing continuance usage intention of Learning management systems by university instructors: The Blackboard system case". International Journal of Web Information Systems, 11(4), 491-509.

https://doi.org/10.1108/IJWIS-03-2015-0008

Neo, M., Park, H., Lee, M., Soh, J., & Oh, J. (2015). Technology Acceptance of Healthcare E-Learning Modules: A Study of Korean and Malaysian Students’ Perceptions. The Turkish Online Journal of Educational Technology, 14(2), 181-194.

Newell, S., Wu, B., Leingpibul, D., & Jiang, Y. (2016). The importance of corporate and salesperson expertise and trust in building loyal business-to-business relationships in China. Journal of Personal Selling & Sales Management, 36(2), 1-14. https://doi.org/10.1080/08853134.2016.1190656

Newjincin. (2021, October 14). Employment rate and graduation destination of Chengdu Higher Vocational College. www.newjincin.com

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

O’Cass, A., & Fenech, T. (2003). Web retailing adoption: exploring the nature of internet users web retailing behavior. Journal of Retailing and Consumer Services, 10(2), 81-94.

Pappas, I. O., Pateli, A. G., Giannakos, M. N., & Chrissikopoulos, V. (2014). Moderating Effects of Online Shopping Experience on Customer Satisfaction and Repurchase Intentions. International Journal of Retail & Distribution Management, 42, 187-204. http://dx.doi.org/10.1108/IJRDM-03-2012-0034

Park, E., & Park, M. (2020). Factors of the Technology Acceptance Model for Construction IT. Applied Sciences, 10(22), 1-15. https://doi.org/10.3390/app10228299

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of educational research, 99(6), 323-338.

Shi, S., & Chow, W. C. (2015). Trust development and transfer in social commerce: prior experience as moderator. Industrial Management & Data Systems, 115(7), 1182-1203.

Soper, D. S. (n.d.). A-priori Sample Size Calculator for Structural Equation Models. Software. www.danielsoper.com/statcalc/default.aspx

Strating, M. M., Suurmeijer, T. P., & van Schuur, W. H. (2006). Disability, social support, and distress in rheumatoid arthritis: results from a thirteen-year prospective study. Arthritis and Rheumatism, 55, 736-744.

Textor, C. (2022, January 13). Number of students at secondary vocational schools in China 2010-220. Statista. https://www.statista.com/statistics/227035/number-of-students-at-secondary-vocational-schools-in-china

Triandis, H. C. (1971). Attitude and Attitude Change. John Wiley and Sons.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The Behavioral Consequences of Service Quality. Journal of Marketing, 60(2), 31-46. https://doi.org/10.2307/1251929

Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2006). Service Marketing. Mc Graw-Hill