Investigating the Effects of Subjective Norms and Trialability on English Teachers` Attitude toward the Use of Technology

Authors

  • Somaye Davoodi Department of English Language, Shiraz Branch, Islamic Azad University, Shiraz
  • Leila Akbarpour Department of English Language, Shiraz Branch, Islamic Azad University, Shiraz
  • Ehsan Hadipour Department of English Language, Shiraz Branch, Islamic Azad University, Shiraz

DOI:

https://doi.org/10.21580/vjv10i17431

Keywords:

Attitude, perceived usefulness, perceived ease of use, trialability, subjective norm

Abstract

Despite the availability of many technological resources in academic settings and their determinant effect in the improvement of teaching and learning, it seems that teachers rarely used them. The aim of the present study is to investigate the effect of psychological variables on teachers` attitudes regarding technology use.  In this research, most applicable models like the 'technology acceptance model', 'theory of reasoned action', and 'innovation diffusion theory' are used as the foundation for developing a conceptual framework. Variables such as 'perceived usefulness', 'perceived ease of use', 'trialability', 'subjective norms', and 'attitude' are recaptured from these models. The participants of the present research are high school English language teachers in Shiraz. The researcher uses stratified sampling to identify a representative sample from the population. The present study is administered by using five questionnaires to assess variables. The data are analyzed by means of path analysis. According to the obtained result, perceived ease of use is found to be affected by subjective norms and trialability, which in turn influenced the attitude of teachers toward using technology in their teachings. The results propose that the most influential factor on attitude is trialability (β= 0.38). On the other hand, perceived usefulness has a significant, influential effect on attitude. Subjective norms has an indirect but important effect on attitude.

Downloads

Download data is not yet available.

References

Adam, L., & Wood, F. (1999). An investigation of the impact of information and communication technologies in sub-Saharan Africa. Journal of Information Science, 25(4), 307–318. https://doi.org/10.1177/016555159902500407

Agarwal, R., & Prasad, J. (1997). The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies. Decision Sciences, 28(3), 557–582. https://doi.org/https://doi.org/10.1111/j.1540-5915.1997.tb01322.x

Al-Zaidiyeen, N. J., Mei, L. L., & Fook, F. S. (2010). Teachers’ Attitudes and Levels of Technology Use in Classrooms: The Case of Jordan Schools. International Education Studies, 3(2), p211. https://doi.org/10.5539/ies.v3n2p211

Albirini, A. (2006). Teachers’ attitudes toward information and communication technologies: the case of Syrian EFL teachers. Computers & Education, 47(4), 373–398. https://doi.org/https://doi.org/10.1016/j.compedu.2004.10.013

Bourgonjon, J., De Grove, F., De Smet, C., Van Looy, J., Soetaert, R., & Valcke, M. (2013). Acceptance of game-based learning by secondary school teachers. Computers and Education, 67, 21–35. https://doi.org/10.1016/j.compedu.2013.02.010

Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming, second edition. In Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, Second Edition. https://doi.org/10.4324/9780203805534

Dastorani, M., & Khoshneshin, Z. (2017). An Analytic Review on the Factors that Affect Technology Acceptance Model (TAM) in Iranian Universities. Interdisciplinary Journal of Virtual Learning in Medical Sciences, In Press. https://doi.org/10.5812/ijvlms.56028

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/10.2307/249008

Delice, M. (2009). Explanation of police officers’ information technology acceptance using the technology acceptance model and social cognitive theory.

Doll, W., Hendrickson, A., & Deng, X. (1998). Using Davis’s Perceived Usefulness and Ease‐of‐use Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis. Decision Sciences, 29, 839–869. https://doi.org/10.1111/j.1540-5915.1998.tb00879.x

Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis: A Global Perspective.

Isaac, O., Abdullah, Z., Ramayah, T., Mutahar, A., & Alrajawy, I. (2016). Perceived Usefulness, Perceived Ease of Use, Perceived Compatibility, and Net Benefits: an empirical study of internet usage among employees in Yemen.

Jung, H.-J. (2015). Fostering an English Teaching Environment: Factors Influencing English as a Foreign Language Teachers’ Adoption of Mobile Learning. Informatics in Education, 14, 219–241. https://doi.org/10.15388/infedu.2015.13

Junhong, L. (2009). A Survey of EFL Learners’ Attitudes toward Information and Communication Technologies. English Language Teaching, 2. https://doi.org/10.5539/elt.v2n4p101

Krejcie, R., & Morgan, D. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement - EDUC PSYCHOL MEAS, 30, 607–610. https://doi.org/10.1177/001316447003000308

Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.-N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees’ Intentions to use E-Learning Systems. Educational Technology & Society, 14, 124–137.

Marcinkiewicz, H., & Regstad, N. (1996). Using Subjective Norms To Predict Teachers’ Computer Use. Journal of Computing in Teacher Education, 13. https://doi.org/10.1080/10402454.1996.11008223

Mutahar, A., Norzaidi, M., Ramayah, T., Isaac, O., & Alrajawy, I. (2017). Integration of Innovation Diffusion Theory (IDT) and Technology Acceptance Model (TAM) to Understand Mobile Banking Acceptance in Yemen: The Moderating Effect of Income. International Journal of Soft Computing, 12, 164–177.

Nan, Z., Guo, X., & Chen, G. (2008). IDT-TAM integrated model for IT adoption. Qinghua Daxue Xuebao/Journal of Tsinghua University, 13, 306–311. https://doi.org/10.1016/S1007-0214(08)70049-X

Ntemana, T., & Olatokun, W. (2012). Analyzing the Influence of Diffusion of Innovation Attributes on Lecturers’ Attitude Towards Information and Communication Technologies. Human Technology An Interdisciplinary Journal On Humans In ICT Environments, 8, 179–197. https://doi.org/10.17011/ht/urn.201211203034

Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society, 12, 150–162.

Rabaa’i, A. (2018). Extending the Technology Acceptance Model (TAM) to assess Students’ Behavioural Intentions to adopt an e-Learning System: The Case of Moodle as a Learning Tool.

Rogers, E. M. (1995). Diffusion of Innovations (4th Edition). New York: Free Press.

Shiau, S., Huang, C.-Y., Yang, C.-L., & Juang, J.-N. (2018). A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability, 10, 3447. https://doi.org/10.3390/su10103447

Smith, J. (2006). The Effect Of Social Presence On Teacher Technology Acceptance, Continuance Intention, And Performance In An Online Teacher Professional Development Course.

Subramanian, G. (1994). A Replication of Perceived Usefulness and Perceived Ease of Use Measurement*. Decision Sciences, 25, 863–874. https://doi.org/10.1111/j.1540-5915.1994.tb01873.x

Tran, T. C. T., & Cheng, M. S. (2017). Adding Innovation Diffusion Theory to Technology Acceptance Model: Understanding Consumers’ Intention to Use Biofuels in Viet Nam. International Review of Management and Business Research, 6, 595.

Tri Anni, C., Sunawan, S., & Haryono, H. (2018). School Counselors’ Intention to Use Technology: The Technology Acceptance Model. Turkish Online Journal of Educational Technology, 17.

Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11, 342–365. https://doi.org/10.1287/isre.11.4.342.11872

Venkatesh, V., & Davis, F. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Wingo, N., Ivankova, N., & Moss, J. (2017). Faculty Perceptions about Teaching Online: Exploring the Literature Using the Technology Acceptance Model as an Organizing Framework. Online Learning, 21. https://doi.org/10.24059/olj.v21i1.761

Yunus, M. (2014). Information and Knowledge Management Diffusion of Innovation, Consumer Attitudes and Intentions to use Mobile Banking. Information and Knowledge Management, 4(10), 12–18. Retrieved from www.iiste.org

Downloads

Published

2021-02-22

Issue

Section

Articles