Abstract
This study tests the validity of an extended theory of planned behaviour (TPB) to explain teachers’ intention to use technology for teaching and learning. Five hundred and ninety two participants completed a survey questionnaire measuring their responses to eight constructs which form an extended TPB. Using structural equation modelling, the results showed that the constructs in the extended TPB were significant in explaining teachers’ intention to use technology in their work. Among the constructs in the research model, attitude towards computer use had the largest positive influence on technology usage intention, followed by perceived behavioral control. However, subjective norm had a negative impact on intention. The inclusion of the antecedent variables had also strengthened the ability of the extended TPB model to explain intention. This study contributes to the growing discussions in applying psychological theories to explain behavioral intention in educational contexts.
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References
Ajzen, I. (1985). From intention to action: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–40). New York: Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32, 665–683.
Ajzen, I. (2005). Attitudes, personality and behaviour (2nd ed.). Maidenhead, Berkshire, England: Open University Press.
Ajzen, I., & Manstead, A. S. (2007). Changing health-related behaviors: An approach based on the theory of planned behavior. In M. Hewstone, H. A. W. Schut, J. B. F. De Wit, K. Van Den Bos, & M. S. Stroebe (Eds.), The scope of social psychology: Theory and applications (pp. 43–63). New York: Psychology Press.
AlQudah, A. A. (2014). Accepting Moodle by academic staff at the University of Jordan: Applying and extending TAM in technical support factors. European Scientific Journal, 10, 183–201.
Amoako-Gyampah, K. (2007). Perceived usefulness, user involvement and behavioral intention: An empirical study of ERP implementation. Computers in Human Behavior, 23, 1232–1248.
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behavior: A meta-analytic review. British Journal of Social Psychology, 40, 471–499.
Azam, S., & Lubna, N. (2013). Mobile phone usage in Bangladesh: The effects of attitude towards behaviour and subjective norm. Annamalai International Journal of Business Studies & Research, 5, 25–34.
Bagozzi, R. P. (2007). The legacy of the Technology Acceptance Model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8, 244–254.
Bakkenes, I., Vermunt, J. D., & Wubbels, T. (2010). Teacher learning in the context of educational innovation: Learning activities and learning outcomes of experienced teachers. Learning and Instruction, 20, 533–548.
Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The internet as an illustration. International Journal of Service Industry Management, 12, 423–450.
Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29, 399–426.
Burnkrant, R. E., & Page, T. J. (1988). The structure and antecedents of the normative and attitudinal components of Fishbein’s theory of reasoned action. Journal of Experimental Social Psychology, 24(1), 66–87.
Carmines, E., & Mclver, J. (1981). Analyzing models with unobservable variables: Analysis of covariance structures. In G. Bohmstedt & E. Borgatta (Eds.), Social measurement: Current issues (pp. 65–115). Beverly Hills, CA: Sage.
Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39, 297–311.
Chen, R. J. (2010). Investigating models for preservice teachers’ use of technology to support student-centered learning. Computers & Education, 55, 32–42.
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, 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, 982–1003.
Downs, D. S., & Hausenblas, H. A. (2005). The theories of reasoned action and planned behavior applied to exercise: A meta-analytic update. Journal of Physical Activity & Health, 2, 76–97.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intension and behavior: An introduction to theory and research. Reading, MA: Addison Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 48, 39–50.
Fornell, C., Tellis, G. J., & Zinkhan, G. M. (1982). Validity assessment: A structural equations approach using partial least squares. In Proceedings of American Marketing Association Educators’ Conference (pp. 405–409). Chicago: AMA.
George, J. F. (2004). The theory of planned behavior and internet purchasing. Internet Research, 14, 198–212.
Georgina, D. A., & Olson, M. R. (2008). Integration of technology in higher education: A review of faculty self-perceptions. The Internet and Higher Education, 11, 1–8.
Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28, 2340–2350.
Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H. (2002). A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. Journal of Sport and Exercise Psychology, 24, 3–32.
Hair, J. F, Jr, Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis: A global perspective. New Jersey: Prentice-Hall International.
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40, 440–465.
Hoelter, J. W. (1983). The analysis of covariance structures goodness-of-fit indices. Sociological Methods & Research, 11(3), 325–344.
Hopp, T. M. (2013). Subjective norms as a driver of mass communication students: Intentions to adopt new media production technologies. Journalism and Mass Communication Educator, 68, 348–364.
Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology. London, UK: Sage.
Hsiao, W. (2011). Technology integration: Preparing in-service teachers for teaching a digital generation. International Journal of Technology, Knowledge & Society, 7, 17–28.
Hu, H. F., Al-Gahtani, S. S., & Hu, P. J. H. (2013). Examining the moderating role of gender in Arabian workers’ acceptance of computer technology. Communications of the Association for Information Systems, 33, 47–66.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
Hu, P. J., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information & Management, 41, 227–241.
Huang, R. T., Deggs, D., Machtmes, K., & Rouge, B. (2011). Faculty online technology adoption: The role of management support and organizational climate. Online Journal of Distance Learning Administration, 14. Retrieved http://www.westga.edu/~distance/ojdla/summer142/huang_142.html
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.
Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption. Testing the UTAUT model. Information & Management, 48, 1–8.
Kiraz, E., & Ozdemir, D. (2006). The relationship between educational ideologies and technology acceptance in pre-service teachers. Educational Technology & Society, 9, 152–165.
Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.
Lee, J., Cerreto, F. A., & Lee, J. (2010). Theory of planned behavior and teachers’ decisions regarding use of educational technology. Educational Technology & Society, 13, 152–164.
Lee, R., Murphy, J., & Swilley, E. (2009). The moderating influence of hedonic consumption in an extended theory of planned behavior. Service Industries Journal, 29, 539–555.
Lumpe, A. T., Haney, J. J., & Czerniak, C. M. (1998). Science teacher beliefs and intentions to implement science-technology-society (STS) in the classroom. Journal of Science Teacher Education, 9(1), 1–24.
Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted learning, 21, 387–395.
Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519–530.
Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2, 173–191.
Ministry of Education (MOE) Singapore (2008). Third masterplan for ICT in education. Retrieved June 23, 2014, from http://www.moe.gov.sg/media/press/2008/08/moe-launches-third-masterplan.php
Ndubisi, N. (2006). Factors of online learning adoption: A comparative juxtaposition of the theory of planned behaviour and the technology acceptance model. International Journal on E-Learning, 5, 571–591.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59, 999–1007.
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.
Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19, 417–433.
Rainbow, S. W., & Sadler-Smith, E. (2003). Attitudes to computer-assisted learning amongst business and management students. British Journal of Educational Technology, 34, 615–624.
Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184.
Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York: Routledge.
Riemenschneider, C. K., & McKinney, V. R. (2001). Assessing belief differences in small business adopters and non-adopters of web-based e-commerce. The Journal of Computer Information Systems, 42, 101–107.
Robert, P., & Henderson, R. (2000). Information technology acceptance in a sample of government employees: A test of the technology acceptance model. Interacting with Computers, 12, 427–443.
Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64, 683–696.
Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012). Exploring factors that predict preservice teachers’ intentions to use Web 2.0 technologies using decomposed theory of planned behavior. Journal of Research on Technology in Education, 45, 171–195.
Salleh, H. (2003). A qualitative study of Singapore primary school teachers’ conceptions. Teaching and Learning, 24, 105–115.
Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling. New York: Routledge.
Sheeran, P., Norman, P., & Orbell, S. (1999). Evidence that intentions based on attitudes better predict behavior than intentions based on subjective norms. European Journal of Social Psychology, 29, 403–406.
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325–343.
Shih, Y. Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Research, 14, 213–223.
Shiue, Y. M. (2007). Investigating the sources of teachers’ instructional technology use through the decomposed theory of planned behavior. Journal of Educational Computing Research, 36, 425–453.
Smarkola, C. (2008). Efficacy of a planned behavior model: Beliefs that contribute to computer usage intentions of student teachers and experienced teachers. Computers in Human Behavior, 24, 1196–1215.
Spiller, J., Vlasic, A., & Yetton, P. (2007). Post-adoption behavior of users of internet service providers. Information & Management, 44, 513–523.
Sugar, W., Crawley, F., & Fine, B. (2004). Examining teachers’ decisions to adopt new technology. Educational Technology and Society, 7, 201–213.
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 561–570.
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(1), 302–312.
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 and Education, 57, 2432–2440.
Teo, T. (2014). Unpacking teachers’ acceptance of technology: Tests of measurement invariance and latent mean differences. Computers & Education, 75, 127–135.
Teo, T., & Fan, X. (2013). Coefficient alpha and beyond: Issues and alternatives for educational research. The Asia-Pacific Education Researcher, 22(2), 209–213.
Teo, T., & van Schaik, P. (2009). Understanding technology acceptance in pre-service teachers: A structural-equation modeling approach. Asia-Pacific Education Researcher (De La Salle University Manila), 18(1), 47–66.
Teo, T., & Tan, L. (2011). The Theory of Planned Behaviour (Theory of Planned Behavior) and pre-service teachers’ technology acceptance: A validation study using structural equation modelling. Journal of Technology and Teacher Education, 20(1), 89–104.
Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142.
Teo, T., & Zhou, M. (In Press). The influence of teachers’ conceptions of teaching and learning on their technology acceptance. Interactive Learning Environments.
Teo, T., Koh, N. K., & Lee, C. B. (2011). Teachers’ intention to teach financial literacy in Singapore: A path analysis of an extended Theory of Planned Behaviour (TPB). Asia-Pacific Education Researcher, 20(2), 410–419.
Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the Technology Acceptance Model (TAM). Journal of Computer Assisted Learning, 24(2), 128–143.
Terry, D. J., & Hogg, M. A. (1996). Group norms and the attitude-behavior relationship: A role for group identification. Personality and Social Psychology Bulletin, 22, 776–793.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15, 124–143.
Trafimow, D., & Finlay, K. A. (1996). The importance of subjective norms for a minority of people: Between subjects and within-subjects analyses. Personality and Social Psychology Bulletin, 22, 820–828.
van Raaij, E., & Schepers, J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50, 838–852.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186–204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
Wang, L., Rau, P. P., & Salvendy, G. (2011). Older adults’ acceptance of information technology. Educational Gerontology, 37, 1081–1099.
Williams, M. D., Rana, N. P., Dwivedi, Y. K., & Lal, B. (2011). Is UTAUT really used or just cited for the sake of it? A systematic review of citations of UTAUT’s originating article. Paper presented at European Council of International Schools.
Wong, K., Osman, R. B., Goh, P. C., & Rahmat, M. K. (2013). Understanding student teachers’ behavioural intention to use technology: Technology Acceptance Model (TAM) validation and testing. International Journal of Instruction, 6, 89–104.
Wu, C., Li, C., & Tsai, C. (2013). Factors determining of effects of teachers’ web-based teaching platform usage: Using UTAUT to explore. Journal of Internet Technology, 14, 919–928.
Zhao, Y., Tan, S. H., & Mishra, P. (2001). Technology, teaching and learning: Whose computer is it? Journal of Adolescent and Adult Literacy, 44, 348–381.
Zolait, A. S. (2014). The nature and components of perceived behavioural control as an element of theory of planned behaviour. Behaviour & Information Technology, 33, 65–84.
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Teo, T., Zhou, M. & Noyes, J. Teachers and technology: development of an extended theory of planned behavior. Education Tech Research Dev 64, 1033–1052 (2016). https://doi.org/10.1007/s11423-016-9446-5
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DOI: https://doi.org/10.1007/s11423-016-9446-5