Investigating students’ intentions to use ICT: A comparison of theoretical models

Abstract

In the technology acceptance studies, both the theory of reasoned action and the technology acceptance model have been widely adopted to study the factors that influence users’ technology usage intentions. While these frameworks have been mostly tested in Western nations, there has been a little effort to apply these frameworks in non-Western nations. With the globalization of education and technology, there is an urgent demand to know whether TRA and TAM apply in another culture. This study compared TRA, TAM and integrated frameworks that best explained or predicted students’ technology usage intention. Structural equation model was employed to perform the data analysis collected from 487 university students. The results showed that there were no differences in predictive strength of behavioral intention among the three models. Thus, the predictive strength of the three models was similar. This study contributed to the ongoing discourses in employing theoretical models to understand undergraduate students’ behavioral intention in educational contexts in developing countries. Implications, limitations and future studies were discussed.

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References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.

    Article  Google Scholar 

  2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  3. Albarq, A. N., & Alsughayir, A. (2013). Examining theory of reasoned action in internet banking using SEM among Saudi consumers. International Journal of Marketing Practices, 1(1), 16–30.

    Google Scholar 

  4. Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102.

    Article  Google Scholar 

  5. Al-Qirim, N., Rouibah, K., & Yammahi, M. A. (2018a). Learning orientations of IT higher education students in UAE University. Education and Information Technologies, 23(1), 129–142.

    Article  Google Scholar 

  6. Al-Qirim, M., Rouibah, K., & Yammahi, M. A. (2018b). Towards a personality understanding of information technology students and their IT learning in UAE University. Education and Information Technologies, 23(1), 29–40.

    Article  Google Scholar 

  7. Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52, 65–71.

    Article  Google Scholar 

  8. Bagozzi. (2007). The legacy of technology acceptance model and a proposal for paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254.

    Article  Google Scholar 

  9. Birch, A., & Irvine, V. (2009). Preservice teachers’ acceptance of ICT integration in theclassroom: applying the UTAUT model. Educational Media International, 46(4), 295–315.

    Article  Google Scholar 

  10. Buabeng-Andoh, C., & Yidana, I. (2014). An investigation of secondary school students’ attitudes toward pedagogical use of ICT in learning in Ghana. Interactive Technology and Smart Education, 11(4), 302–314.

    Article  Google Scholar 

  11. Chen, R. (2010). Investigating models for preservice teachers’ use of technology tosupport student-centered learning. Computer and Education, 55, 32–42.

    Article  Google Scholar 

  12. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  13. Chuang, H. H., Weng, C. Y., & Huang, F. C. (2015). A structure equation model among factors of teachers' technology integration practice and their TPCK. Computers & Education, 86, 182–191.

    Article  Google Scholar 

  14. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah: Lawrence Erlbaum.

    Google Scholar 

  15. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  16. 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.

    Google Scholar 

  17. Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information Management, 36(1), 9–21.

    Article  Google Scholar 

  18. Drennan, J., Kennedy, J., & Pisarksi, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331–338.

    Article  Google Scholar 

  19. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading: Addison Wesley.

    Google Scholar 

  20. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  21. Gay, L. R., & Airasian, P. W. (2009). Educational research: Competencies for analysis and applications, student value edition. Upper Saddle River, NJ: Merrill.

  22. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (pls--sem). Los Angeles: Sage Publications.

    Google Scholar 

  23. Hassandoust, F., Logeswaran, R., & Kazerouni, M. F. (2011). Behavioral factors influencing virtual knowledge sharing: Theory of reasoned action. Journal of Applied Research in Higher Education, 3(2), 116–134.

    Article  Google Scholar 

  24. Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind. New York: McGraw-Hill.

    Google Scholar 

  25. Hosseini, Z., Gharghani, Z. G., Mansoori, A., Aghamolaei, T., & Nasrabadi, M. M. (2015). Application of the theory of reasoned action to promoting breakfast consumption. Medical Journal of the Islamic Republic of Iran, 29, 289–297.

    Google Scholar 

  26. 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(2), 12–24.

    Google Scholar 

  27. Kim, B., & Park, M. J. (2017). Effect of personal factors to use ICTs on e-learning adoption: Comparison between learner and instructor in developing countries. Information Technology for Development, 23(2), 1–27.

    Google Scholar 

  28. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guildford Press.

  29. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information Management, 40, 191–204.

    Article  Google Scholar 

  30. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.

    Article  Google Scholar 

  31. Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour & Information Technology, 36(10), 1081–1100.

    Article  Google Scholar 

  32. Nicholas-Omoregbe, O. S., Azeta, A. A., Chiazor, I. A., & Omoregbe, N. (2017). Predicting the adoption of E-learning management system: A case of selected private universities in Nigeria. Turkish Online Journal of Distance Education, 18(2), 106–121.

    Article  Google Scholar 

  33. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.

    Google Scholar 

  34. 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(3), 150–162.

    Google Scholar 

  35. Peslak, A., Ceccucci, W., & Sendall, P. (2011). An empirical study of social networking behavior using theory of reasoned action. 2011 CONISAR Proceedings. Conference for Information Systems Applied Research, Wilmington North Carolina, USA.

  36. Raza, S. A., Umer, A., Qazi, W., & Makhdoom, M. (2017). The effects of attitudinal, normative, and control beliefs on M-learning adoption among the students of higher education in Pakistan. Journal of Educational Computing Research. https://doi.org/10.1177/0735633117715941.

  37. Rienties, B., Giesbers, B., Lygo-Baker, S., Ma, H. W. S., & Rees, R. (2016). Why some teachers easily learn to use a new virtual learning environment: A technology acceptance perspective. Interactive Learning Environment, 24(3), 539–552.

    Article  Google Scholar 

  38. Sarfo, F. K., & Ansong-Gyimah, K. (2011). Ghanaian senior high school students’ access to and experiences in the use of information and communication technology. In A. Mendez-Vilas (Ed.), Education in a Technological world: Communicating current and emerging research and technological efforts(pp. 216–222). Badajoz: Formatex Research Centre.

  39. Simbolon, S. (2015). Application of theory of reasoned action in predicting the consumer behavior to buy the Toyota Avanza Veloz at PT. Putera Auto Perkasa Medan. Journal of Asian Scientific Research, 5(7), 357–372.

    Article  Google Scholar 

  40. Smarkola, C. (2007). Technology acceptance predictors among student teachers and experienced classroom teachers. Journal of Educational Computing Research, 31, 65–82.

    Article  Google Scholar 

  41. Song, Y., & Kong, S.-C. (2017). Investigating students’ acceptance of a statistics learning platform using technology acceptance model. Journal of Educational Computing Research, 0(0), 1–33.

    Google Scholar 

  42. Srite, M. (2006). Culture as an explanation of technology acceptance differences: An empirical investigation of Chinese and US users. Australasian Journal of Information Systems, 14(1), 5–26.

    Article  Google Scholar 

  43. Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three-country study. Information Management, 33(1), 1–11.

    Article  Google Scholar 

  44. Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153–163.

    Article  Google Scholar 

  45. Tarhini, A., Hone, K., & Liu, X. (2015). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46(4), 739–755.

    Article  Google Scholar 

  46. Tarhini, A., Teo, T., & Tarhini, T. (2016). A cross-cultural validity of the E-learning Acceptance Measure (ElAM) in Lebanon and England: A confirmatory factor analysis. Education and Information Technologies, 21(5), 1269–1282.

    Article  Google Scholar 

  47. Tarhini, A., Masa'deh, R., Al-Busaidi, K., & Maqableh, M. (2017). Factors influencing students' adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164–182.

    Article  Google Scholar 

  48. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.

    Google Scholar 

  49. Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments, 20(1), 3–18.

    Article  Google Scholar 

  50. Teo, T., & van Schaik, P. (2012). Understanding the intention to use technology by preservice teachers: An empirical test of competing theoretical models. International Journal of Human-Computer Interaction, 28(3), 178–188.

    Article  Google Scholar 

  51. 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 (TAM). Journal of Educational Technology and Society, 11, 265–280.

    Google Scholar 

  52. Teo, T., Zhou, M., & Noyes, J. (2016). Teachers and technology: Development of an extended theory of planned behavior. Educational Technology Research & Development, 64(3), 1–22.

    Google Scholar 

  53. Teo, T., Huang, F., & Hoi, C. K. W. (2017). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475.

  54. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  55. Venkatesk, V., Morris, M. G., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  Google Scholar 

  56. Wong, S. L., & Teo, T. (2009). Investigating the technology acceptance among student-teachers in Malaysia: An application of the technology acceptance model (TAM). The Asia-Pacific Education Researcher, 18(2), 261–272.

    Google Scholar 

  57. Wong, K., Russo, S., & McDowall, J. (2013). Understanding early childhood student teachers’ acceptance and use of interactive whiteboard. Campus-Wide Information Systems, 30(1), 4–16.

    Article  Google Scholar 

  58. Yeou, M. (2016). An investigation of students’ acceptance of moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology Systems, 44(3), 300–318.

    Article  Google Scholar 

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Correspondence to Ali Tarhini.

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Appendix: Survey instrument

Appendix: Survey instrument

This survey is for academic purpose only. All information that is collected in this study will be treated confidentially. At no time will the name of any school or individual be identified. Please use a writing pen to write your answers.

Instruction:Please indicate your response to the following questions by ticking or circling the appropriate letter.

Part I: Demographic Information

figurea

Part II: Your Views on Technology

Using the scale provided, please rate the extent to which you agree or disagree to the following statement regarding the use of computer technology ion the classroom.

Item Strongly Disagree Moderately Disagree Slightly Disagree Neutral Slightly Agree Moderately Agree Strongly Agree
PU1: Using technology enables me to accomplish tasks more quickly        
PU2: Using technology improves my performance        
PU3: Using technology will increase my productivity        
PU4: Using technology enhances my effectiveness.        
PEOU1: I find it easy to use technology to do what I want to do.        
Item Strongly Disagree Moderately Disagree Slightly Disagree Neutral Slightly Agree Moderately Agree Strongly Agree
PEOU2: My interaction with technology does not require much effort.        
PEOU3: It is easy for me to become skillful at using technology.        
PEOU4: I have control over technology        
PEOU5: I have the knowledge necessary to use technology.        
ATU1: I look forward to those aspects of my job that require me to use technology.        
ATU2: I like working with technology        
ATU3: I have positive feelings towards the use of technology.        
BIU1: I intend to continue to use technology in the future.        
BIU2: I expect that I would use technology in the future.        
BIU3: I plan to use technology in the future.        

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Buabeng-Andoh, C., Yaokumah, W. & Tarhini, A. Investigating students’ intentions to use ICT: A comparison of theoretical models. Educ Inf Technol 24, 643–660 (2019). https://doi.org/10.1007/s10639-018-9796-1

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Keywords

  • Technology acceptance model
  • Structural equation modeling
  • Theory of reasoned action
  • Behavioral intention
  • Theoretical models
  • e-Learning