Abstract
It is becoming necessary for trainee teachers to willingly accept technology as a tool for learning, effective teaching and assessment. The aim of this study is to measure trainee teachers’ perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, attitude towards technology use and behavioural intention to use technology. Data was collected from 203 trainee teachers in Bahrain. We employed structural equation approach to analyse the relationships among the factors affecting trainee teachers’ intention to use technology. Results from structural equation modeling analyses suggested that perceived ease of use was a moderate predictor of perceived usefulness and attitude towards use and perceived usefulness was a strong predictor of behavioural intention to use technology. However, subjective norm and attitude towards technology use were not found significantly associated with behavioural intention to use technology. This study has contributed to the growing body of studies on the technology acceptance model and it is the first study in the Kingdom of Bahrain that has explored trainee teachers’ intention to use technology.
Similar content being viewed by others
References
Afari, E., & Khine, M. S. (2016). Students’ intention to use computer technology: A structural equation modelling analysis. Int. J. Quantitative Research in Education, 3(1/2), 41–57.
Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Al-Arrayed, S. M. (1987). Education in Bahrain - 1919-1986: An analytical study of problems and progress. In Durham theses. Durham: University. Available at Durham E- Theses Online http://etheses.dur.ac.uk/6662/.
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389–412.
Anderson, J. (2010). ICT transforming: A regional guide. Bangkok: Unesco.
Arbuckle, J. L. (2017). Amos (version 25.0) [computer program], Chicago: IBM SPSS.
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: Personal computer adoption and uses as an illustration. Technology Studies, 2, 285–309.
Becker, H. (2001). How are teachers using computers in instruction? Paper presented at the 2001 meeting of the American Educational Research Association. WA: Seattle.
Bertrand, M., & Bouchard, S. (2008). Applying the technology acceptance model to VR with people who are favourable to its use. Journal of Cyber Therapy & Rehabilitation, 1(2), 200–210.
Birch, D., & Burnett, B. (2009). Bringing academics on board: Encouraging institution-wide diffusion of e-learning environments. Australasian Journal of Educational Technology, 25(1), 117–134 http://www.ascilite.org.au/ajet/ajet25/birch.html.
Brislin, R. (1970). Back translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1, 185–216.
Chang, H. H., & Wang, I. C. (2008). An investigation of user communication behavior in computer mediated environments. Computers in Human Behavior, 24, 2336–2356.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly., 19, 189–211.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
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.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(1), 1111–1132.
DeVellis, R. F. (2012). Scale development: Theory and applications (3rd ed.). Thousand Oaks, California: Sage.
Ercikan, K. (1998). Translation effects in international assessments. International Journal of Educational Research, 29, 543–553.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research, Addision-Wesley, Reading, MA.
Fornell, C., & Larker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Ghavifekr, S., & Rosdy, W. A. W. (2015). Teaching and learning with technology: Effectiveness of ICT integration in schools. International Journal of Research in Education and Science, 1(2), 175–191.
Ghavifekr, S., Ahmad Zabidi, A. R., Muhammad Faizal, A. G., Ng, Y. R., Yao, M., & Zhang, T. (2014). ICT integration in education: Incorporation for teaching & learning improvement. Malaysian Online Journal of Educational Technology, 2(2), 24–46.
Harris, S. (2002). Innovative pedagogical practices using ICT in schools in England. Journal of Computer Assisted Learning, 18(4), 449–458.
Huang, H. M., & Liaw, S. S. (2005). Exploring user’s attitudes and intentions toward the web as a survey tool. Computers in Human Behavior, 21, 729–743.
Jhurree, V. (2005). Technology integration in education in developing countries: Guidelines to policy makers. International Education Journal, 6(4), 467–483.
Jones, A. (2004). A review of the research literature on barriers to the uptake of ICT by teachers. Coventry, United Kingdom: Becta.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.
Kline, R. B. (2010). Principles and practices of structural equation modeling (3rd ed.), New York: Guilford press.
Kline, T. J. B., Sulksy, L. M., & Rever-Moriyama, S. D. (2000). Common method variance and specification errors: A practical approach to detection. The Journal of Psychology, 134(4), 401–421.
Lai, P. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management, 14(1), 21–38.
Li, Y., Wang, Q., & Lei, J. (2019). Modeling Chinese Teachers' attitudes toward using Technology for Teaching with a SEM approach. Computers in the Schools, 36(2), 122–141.
Lim, C. P., & Khine, M. S. (2006). Managing teachers’ barriers to ICT integration in Singapore schools. Journal of Technology and Teacher Education, 1(1), 97–125.
Lin, H. F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electron. Commerce Res. Appl., 64, 433–442.
Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers in Education, 52, 599–607.
Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing (JOEUC), 16(1), 59–72.
Mathieson, K., Peacock, K. M. 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.
Mitra, A., & Steffensmeier, T. (2000). Changes in student attitudes and student computer use in a computer-enriched environment. Journal of Research on Computing in Education, 32(1), 417–433.
Moon, J., & Kim, Y. (2001). Extending TAM for a world-wide-web context. Information & Management, 38(1), 217–230.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
Peat, M., & Franklin, S. (2003). Has student learning been improved by the use of online and offline formative assessment opportunities? Australian Journal of Educational Technology, 19(1), 87–99.
Philips, L. A., Calantone, R., & Lee, M. T. (1994). International technology adoption: Behavior structure, demand certainty and culture. Journal of Business & Industrial Marketing, 9(2), 16–28.
Pynoo, B., & van Braak, J. (2014). Predicting teachers’ generative and receptive use of an educational portal by intention, attitude and self-reported use. Computers in Human Behavior, 34, 315–322.
Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. NY: Routledge.
Razzak, N. L. A. (2015). Challenges facing school leadership in promoting ICT integration in instruction in the public schools of Bahrain. Education and Information Technologies, 20(2), 303–318.
Razzak, N. A. (2016). Teachers’ experiences with school improvement projects: The case of Bahraini public schools. Cogent Education, 1–18.
Robert, P., & Henderson, R. (2000). Information technology acceptance in a sample of government employees: A test of the technology acceptance model. Interacting with Computer, 12, 427–443.
Sang, G., Valcke, M., van Braak, J., Tondeur, J., & Zhu, C. (2011). Predicting ICT integration into classroom teaching in Chinese primary schools: Exploring the complex interplay of teacher-related variables. Journal of Computer Assisted Learning, 27, 106–172.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103.
Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling (4th ed.), New York: Routledge.
Taylor, S., & Todd, P. A. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(2), 561–570.
Teo, T. (2009). Evaluating the intention to use technology among student teachers: A structural equation modeling approach. International Journal of Technology in Teaching Learning, 5(2), 106–118.
Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: Applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65–79.
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57, 2432–2440.
Teo, T., & Lee, C. B. (2012). Assessing the factorial validity of the metacognitive awareness inventory (MAI) in an Asian country: A confirmatory factor analysis. The International Journal of Educational and Psychological Assessment, 10(2), 92–103.
Teo, T., & Van Schaik, P. (2009). Understanding technology acceptance in pre-service teachers: A structural-equation modelling approach. Asia-Pacific Education Researcher, 18(1), 47–66.
Teo, T., Luan, W. S., & Sing, C. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: An application of the technology acceptance model. Journal of Educational Technology and Society, 11, 265–280.
Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model (TAM). Computers & Education, 53(3), 1000–1009.
Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67(3), 749–766.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124–143.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Science, 39(2), 273–312.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Towards a unified view. MIS Quarterly, 27(3), 425–478.
Warwick, D. P., & Osherson, S. (1973). Comparative analysis in the social sciences. In D. P. Warwick & S. Osherson (Eds.), Comparative research methods: An overview (pp. 3–41). Englewood Cliffs, NJ: Prentice-Hall.
Wu, I. L., & Chen, J. L. (2005). An extension of trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study. International Journal of Human Computer Studies, 62, 784–808.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Eksail, F.A.A., Afari, E. Factors affecting trainee teachers’ intention to use technology: A structural equation modeling approach. Educ Inf Technol 25, 2681–2697 (2020). https://doi.org/10.1007/s10639-019-10086-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-019-10086-2