INFLUENCING FACTORS OF BEHAVIORAL INTENTION TO USE ONLINE LEARNING AMONG HIGHER VOCATIONAL STUDENTS IN CHENGDU
Keywords:
Online Learning, Satisfaction, Trust, Attitude, Behavioral IntentionAbstract
This research examines the influencing factors of behavioral intention to use online learning among higher vocational students in Tianfu Vocational College of Chengdu, China. The research model involves perceived ease of use, perceived usefulness, attitude, trust, satisfaction and behavioral intention to use online learning. Population and sample size of 500 second-grade students were accounted for the data collection by questionnaire distribution. The sample techniques include purposive, stratified random, and convenience sampling. For preliminary test, the results of index of item objective congruence (IOC) and Cronbach’s Alpha coefficient of 50 samples were acceptable to proceed the data analysis. Afterwards, confirmatory factor analysis (CFA) and structural equation modeling (SEM) approach were carried out to confirm data’s validity, reliability and goodness of fit. In results, perceived ease of use significantly influenced perceived usefulness and attitude. Trust had a significant influence on satisfaction towards behavioral intention. In contrary, perceived usefulness had no significant influence on attitude and behavioral intention, and attitude did not have a significant influence on behavioral intention. This research contributes to academic researchers and school management executive to efficiently promote the use of online learning during the concern of COVID-19 pandemic.
References
Ajzen, I. (1991). The Theory of Planned behavior. Organizational Behavior and Human Decision Processes, 50, 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.
Babin, B., & Babin, L. (2001). Seeking something different? A model of schema typicality, consumer affect, purchase intentions and perceived shopping value. Journal of Business Research, 54(2), 89-96.
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 K. A. Bollen & J. S. Long (Eds.). Testing structural equation models (pp.136–162). SAGE Publication, Inc.
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.
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.
Corbitt, B., Thanasankit, T., & Yi, H. (2003). Trust and E-Commerce: A Study of Consumer Perceptions. Electronic Commerce Research and Applications, 2(3), 203-215. https://doi.org/10.1016/S1567-4223(03)00024-3
Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3),184-201.
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.
Deng, Z., Lu, Y., Wei, K. K., & Zhang, J. (2010). Understanding customer satisfaction and loyalty:an empirical study of mobile instant messages in China. International Journal of Information Management, 30(4), 289-300.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics: And Sex and Drugs and Rock “N” Roll (4th ed.). Sage.
Fornell, C. (1992). A national customer satisfaction barometer: the Swedish experience. Journal of Marketing, 56(1),6-21.
Fornell, C. G., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gray, D., & Daymond, J. (2010). The Influence of student engagement levels on satisfaction and behavioural 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.
Lee, S., Yoo, Y., & Yun, S. (2015). Sharing my knowledge? An interactional perspective. Journal of Managerial Psychology, 30(8), 986-1002. https://doi.org/10.1108/JMP-11-2013-0355
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., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. The DATA BASE for Advances in Information Systems, 32(3), 86–112.
MOE. (2022). Vocational Education in China. http://www.moe.gov.cn
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.
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
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Pappas, I., Pateli, A., Giannakos, M., & Chrissikopoulos, V. (2014). Moderating effects of online shopping experience on customer satisfaction and repurchase intentions. International Journal of Retail & Distribution Management, 42(3), 187-204.
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.
Taylor, S., & Todd, P. (1995) Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561–570.
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
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. MIS Quarterly, 27(3), 425–478.
Wang, T. Z. (2014). Career Planning and Career Guidance. Northwestern University Press.
Weathington, J. M., Strahan, J. A., & Cooke, B. M. (2012). Social experience induces sex-specific for expression in the amygdala of the juvenile rat. Hormones and Behavior, 62(2), 154–161. https://doi.org/10.1016/j.yhbeh.2012.06.004
Yeh, Y. S., & Li, Y. M. (2009). Building trust in m-commerce: contributions from quality and satisfaction. Online Information Review, 33(6), 1066-1086.
Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12(2), 225–248. https://doi.org/10.1007/s10660-012-9092-x
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
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
