Factors Promoting Teaching Behavior of English Teachers in Primary Schools in Chengdu High-Tech Zone, China
Article Sidebar
Main Article Content
Abstract
Purpose: The study aims to investigate the influence of the teaching behavior of primary school English teachers in Chengdu High-Tech Zone, China. The conceptual framework includes perceived ease of use, perceived usefulness, attitude, subjective norms, behavioral intention, and behavior. Research design, data, and methodology: The population and sample size are 500 primary school English teachers who are 21 years old and above in the Chengdu high-tech zone, China. The researchers used three steps to collect target samples: purpose or judgment, convenience, and snowball sampling. Before the data collection, the validity of the research instrument was assessed by the index of item-objective congruence (IOC) and a pilot test by the Cronbach's alpha coefficient reliability test. In addition, confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to analyze the reliability of study variables and conceptual frameworks. Results: The results show that perceived ease of use significantly influences perceived usefulness. Perceived ease of use and perceived usefulness have a significant influence on attitude. Attitude significantly influences behavioral intention. Behavioral intention significantly influences behavior. Nevertheless, subjective norms have no significant influence on behavioral intention. Conclusions: Educational institutions can develop strategies that address the specific normative influences prevalent within their unique contexts.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Arbuckle, J. (1995). AMOS: Analysis of moment structures user’s guide. Small Waters.
Balau, M. (2018). Exploring the Link between Intention and Behavior in Consumer Research [Paper Presentation]. Proceedings European Integration - Realities and Perspectives, Galati, Romania.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
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.
Chatzoglou, P. D., & Vraimaki, E. (2009). Knowledge-sharing behavior of bank employees in Greece. Business Process Management Journal, 15(2), 245-266.
Chennamaneni, A., Teng, J. T. C., & Raja, M. K. (2012). A unified model of knowledge sharing behaviors: theoretical development and empirical test. Behavior & Information Technology, 31(11), 1097-1115.
Chua, P. Y., Rezaei, S., Gu, M.-L., Oh, Y., & Jambulingam, M. (2018). Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence. Nankai Business Review International, 9(2), 118-142. https://doi.org/10.1108/NBRI-01-2017-0003.
Coolahan, J. (2002). Teacher Education and the Teaching Career in an Era of Lifelong Learning OECD Education (Working Paper No. 2). http://dx.doi.org/10.1787/226408628504
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. http://dx.doi.org/10.1287/mnsc.35.8.982
Fornell, C., & Larcker, D. (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
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31, 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Hong, K. S., Ridzuan, A. A., & Kuek, M. K. (2003). Students' attitudes toward the use of the Internet for learning: A study at a university in Malaysia. Educational Technology & Society. 6(2), 45-49.
Keong, M. L., Thurasamy, R., Sherah, K., & Chiun, L. M. (2012). Explaining intention to use an enterprise resource planning (ERP) system: an extention of the UTAUT model. Business Strategy Series, 13(4), 108-120.
Lee, M. (2009). Understanding the behavioural intention to play online games: An extension of the theory of planned behaviour. Online Information Review, 33(5), 849-872. https://doi.org/10.1108/14684520911001873
Li, Y., & Kitcharoen, S. (2022). Determinants of Undergraduates’ Continuance Intention and Actual Behavior to Play Mobile Games In Chongqing, China. AU-GSB E-JOURNAL, 15(2), 206-214. https://doi.org/10.14456/augsbejr.2022.86
Mafabi, S., Nasiima, S., Muhimbise, E. M., Kasekende, F., & Nakiyonga, C. (2017). The mediation role of intention in knowledge sharing behavior. VINE Journal of Information and Knowledge Management Systems, 47(2), 172-193. https://doi.org/10.1108/VJIKMS-02-2016-0008
Manakul, T., Somabut, A., & Tuamsuk, K. (2023). Smart teaching abilities of junior high school teachers in Thailand. Cogent Education, 10(1), 1-14. http://doi.org/10.1080/2331186X.2023.2186009
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information and Management, 38(4), 217-230.
Musset, P. (2010). Initial Teacher Education and Continuing Training Policies in a Comparative Perspective: Current Practices in OECD Countries and a Literature Review on Potential Effects (OECD Education Working Papers No. 48). http://dx.doi.org/10.1787/5kmbphh7s47h-en
Niemi, H. (2015). Teacher Professional Development in Finland: Towards a More Holistic Approach. Psychology, Society & Education, 7(3), 279-294. http://doi.org/10.25115/psye.v7i3.519
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. Psychometric Theory, 3, 248-292.
OECD. (2009). Creating Effective Teaching and Learning Environments First Results from TALI. OECD.
Pipitwanichakarn, T., & Wongtada, N. (2021). Leveraging the technology acceptance model for mobile commerce adoption under distinct stages of adoption: A case of micro businesses. Asia Pacific Journal of Marketing and Logistics, 33(6), 1415-1436. https://doi.org/10.1108/APJML-10-2018-0448
Sharma, S. K., Chandel, J. K., & Govindaluri, S. M. (2014). Students’ acceptance and satisfaction of learning through course websites. Education, Business, and Society: Contemporary Middle Eastern Issues, 7(2/3),152-166.
Soper, D. S. (2023). A priori sample size calculator for structural equation model [software]. https://www.danielsoper.com/statcalc
Studenmund, A. H. (1992). Using Econometrics: A Practical Guide. Harper Collins.
Toft, M., Schuitema, G., & Thøgersen, J. (2014). The importance of framing for consumer acceptance of the Smart Grid: A comparative study of Denmark, Norway and Switzerland. Energy Research & Social Science, 3, 113–123. http://doi.org/10.1016/j.erss.2014.07.010
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.