Abstract
References
- Aaker, D. A., Kumar, V., Leone, R. P., & Day, G. S. (2013). Marketing Research (11th ed.). John Wiley & Sons, Inc.
- Abbas, H. A., & Hamdy, H. I. (2015). Determinants of continuance intention factor in Kuwait communication market: A case study of Zain-Kuwait. Computers in Human Behavior, 49, 648–657.
- Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior. 63:75–90.
- Aboelmaged, M., & Gebba, T. R. (2013). Mobile banking adoption: An examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development, 2(1), 35–50.
- Agarwal, R. (2000). Individual acceptance of information technologies. In R. W. Zmud (Ed.), Framing the domains of IT management: projecting the future through the past (pp. 85–104). Cincinnati, OH: Pinnaflex Pressgarwal.
- Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Abstract
This study aims to uncover the extension of the Technology Acceptance Model (TAM) in understanding, explaining, and predicting elementary school teachers’ behavior in Indonesia to use online learning technology during the covid-19 pandemic. The TAM model in this study is extended by accounting for four additional variables, which are subjective norms, and job relevance as a predictor of perceived usefulness, also, computer self-efficacy and computer anxiety to predict perceived ease of use. This study uses an online survey method with a purposive sampling technique involving 475 elementary school teachers located in Jakarta, Palu, and Sorong (some of the big cities in Indonesia). The data analysis technique is Structural Equation Modeling (SEM) with the help of IBM SPSS AMOS 21 software. The results of this study show that the TAM model developed in this study can explain and predict the intentions of elementary school teachers in using online learning technology during the covid-19 pandemic as indicated by the support of the hypotheses tested in this study. Overall, TAM is a very popular model for understanding, explaining, and predicting the use of new technology systems. The implications and limitations of this study are also discussed in this article.
References
- Aaker, D. A., Kumar, V., Leone, R. P., & Day, G. S. (2013). Marketing Research (11th ed.). John Wiley & Sons, Inc.
- Abbas, H. A., & Hamdy, H. I. (2015). Determinants of continuance intention factor in Kuwait communication market: A case study of Zain-Kuwait. Computers in Human Behavior, 49, 648–657.
- Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior. 63:75–90.
- Aboelmaged, M., & Gebba, T. R. (2013). Mobile banking adoption: An examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development, 2(1), 35–50.
- Agarwal, R. (2000). Individual acceptance of information technologies. In R. W. Zmud (Ed.), Framing the domains of IT management: projecting the future through the past (pp. 85–104). Cincinnati, OH: Pinnaflex Pressgarwal.
- Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | October 1, 2022 |
| Submission Date | July 7, 2021 |
| Published in Issue | Year 2022 Volume: 23 Issue: 4 |