Skip to main content
Log in

An empirical investigation of computer simulation technology acceptance to explore the factors that affect user intention

  • Short Paper
  • Published:
Universal Access in the Information Society Aims and scope Submit manuscript

Abstract

While computer simulations have been shown to be effective with regard to supporting learning, little effort has been made to explore the factors that affect the intention to use such tools. This paper applies the technology acceptance model and examines two external variables, facilitating conditions and computer self-efficacy, testing a number of hypotheses. The results show that most of the hypotheses the authors developed before the study were supported by the data collected, and further reveal that perceived usefulness is the most important determinant of student intention to use a computer simulation, followed by attitude toward using and computer self-efficacy. Finally, both the implications and limitations of this study are discussed, and further research directions are proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84(2), 191–215 (1977)

    Article  Google Scholar 

  2. Chatzoglou, P.D., Sarigiannidis, L., Vraimaki, E., Diamantidis, A.: Investigating Greek employees’ intention to use web-based training. Comput. Educ. 53(3), 877–889 (2009)

    Article  Google Scholar 

  3. Cheung, R., Vogel, D.: Predicting user acceptance of collaborative technologies: an extension of the technology acceptance model for e-learning. Comput. Educ. 63, 160–175 (2013)

    Article  Google Scholar 

  4. Chin, W.W., Newsted, P.R.: Structural equation modeling analysis with small samples using partial least squares. In: Hoyle, R. (ed.) Statistical Strategies for Small Sample Research, pp. 307–341. Sage Publications, California (1999)

    Google Scholar 

  5. Chow, M., Herold, D.K., Choo, T.M., Chan, K.: Extending the technology acceptance model to explore the intention to use second life for enhancing healthcare education. Comput. Educ. 59(4), 1136–1144 (2012)

    Article  Google Scholar 

  6. Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, 2nd edn. Erlbaum, Hillsdale (1988)

    MATH  Google Scholar 

  7. Compeau, D.R., Higgins, C.A.: Computer self-efficacy: development of a measure and initial test. MIS Q. 19(2), 189–211 (1995)

    Article  Google Scholar 

  8. Dahlan, N., Ramayah, T., Mei, L.L.: Readiness to adopt data mining technologies: an exploratory study of telecommunication employees in Malaysia. Lect. Notes Comput. Sci. 25(69), 75–86 (2002)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)

    Article  Google Scholar 

  11. Fishbein, M., Azjen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)

    Google Scholar 

  12. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)

    Article  Google Scholar 

  13. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L.: Multivariate Data Analysis, 6th edn. Prentice-Hall, New Jersey (2006)

    Google Scholar 

  14. Hill, T., Smith, N.D., Mann, M.F.: Role of efficacy expectations in predicting the decision to use advance technologies: the case of computers. J. Appl. Psychol. 72(2), 307–313 (1987)

    Article  Google Scholar 

  15. Hobson, S.M., Trundle, K.C., Saçkes, M.: Using a planetarium software program to promote conceptual change with young children. J. Sci. Educ. Technol. 19(2), 165–176 (2010)

    Article  Google Scholar 

  16. Holzinger, A., Kickmeier-Rust, M., Wassertheurer, S., Hessinger, M.: Learning performance with interactive simulations in medical education: lessons learned from results of learning complex physiological models with the HAEMOdynamics SIMulator. Comput. Educ. 52(1), 292–301 (2009)

    Article  Google Scholar 

  17. Holzinger, A., Searle, G., Wernbacher, M.: The effect of previous exposure to technology on acceptance and its importance in usability engineering. Univ. Access Inf. Soc. 10(3), 245–260 (2011)

    Article  Google Scholar 

  18. Holzinger, A., Wassertheurer, S., Emberger, W., Neal, L.: Design, development and evaluation of online interactive simulation software for learning human genetics. Elektrotechn. Inf. 125(5), 190–196 (2008)

    Article  Google Scholar 

  19. Huang, T.C.K., Liu, C.C., Chang, D.C.: An empirical investigation of factors influencing the adoption of data mining tools. Int. J. Inf. Manag. 32(3), 257–270 (2012)

    Article  Google Scholar 

  20. Huang, Y.M., Chiu, P.S.: The effectiveness of a meaningful learning-based evaluation model for context-aware mobile learning. Br. J. Educ. Technol. (in press). doi:10.1111/bjet.12147

  21. Huang, Y.M., Liang, T.H.: A technique for tracking the reading rate to identify the e-book reading behaviors and comprehension outcomes of elementary school students. Br. J. Educ. Technol. (in press). doi:10.1111/bjet.12182

  22. Huang, Y.M., Huang, Y.M., Huang, S.H., Lin, Y.T.: A ubiquitous English vocabulary learning system: evidence of active/passive attitudes vs. usefulness/ease-of-use. Comput. Educ. 58(1), 273–282 (2012)

    Article  Google Scholar 

  23. Huang, Y.M., Liu, C.H., Lee, C.Y., Huang, Y.M.: Designing a personalized guide recommendation system to mitigate information overload in museum learning. Educ. Technol. Soc. 15(4), 150–166 (2012)

    Google Scholar 

  24. Igbaria, M., Iivari, J.: The effects of self-efficacy on computer usage. Omega 23(6), 587–605 (1995)

    Article  Google Scholar 

  25. Karaali, D., Gumussoy, C.A., Calisir, F.: Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Comput. Hum. Behav. 27(1), 343–354 (2011)

    Article  Google Scholar 

  26. Lederer, A.L., Maupin, D.J., Sena, M.P., Zhuang, Y. L.: The technology acceptance model and the World Wide Web. Decis. Support Syst. 29(3), 269–282 (2000)

    Article  Google Scholar 

  27. Lee, M.C.: Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation–confirmation model. Comput. Educ. 54(2), 506–516 (2010)

    Article  Google Scholar 

  28. Liang, T.H., Huang, Y.M.: An investigation of reading rate patterns and retrieval outcomes of elementary school students with e-books. Educ. Technol. Soc. 17(1), 218–230 (2014)

    MathSciNet  Google Scholar 

  29. Lin, J.C.C., Lu, H.: Towards an understanding of the behavioral intention to use a web site. Int. J. Inf. Manag. 20(3), 197–208 (2000)

    Article  Google Scholar 

  30. Liu, H.C., Chuang, H.H.: Investigation of the impact of two verbal instruction formats and prior knowledge on student learning in a simulation-based learning environment. Interact. Learn. Environ. 19(4), 433–446 (2011)

    Article  Google Scholar 

  31. Liu, H.C., Su, I.H.: Learning residential electrical wiring through computer simulation: the impact of computer-based learning environments on student achievement and cognitive load. Br. J. Educ. Technol. 42(4), 598–607 (2011)

    Article  Google Scholar 

  32. Liu, T.C.: Developing simulation-based computer assisted learning to correct students’ statistical misconceptions based on cognitive conflict theory, using “correlation” as an example. Educ. Technol. Soc. 13(2), 180–192 (2010)

    Google Scholar 

  33. Lu, J., Liu, C., Yu, C.S., Wang, K.: Determinants of accepting wireless mobile data services in China. Inform Manage-AMSTER 45(1), 52–64 (2008)

    Article  Google Scholar 

  34. Mujacic, S., Debevc, M., Kosec, P., Bloice, M., Holzinger, A.: Modeling, design, development and evaluation of a hypervideo presentation for digital systems teaching and learning. Multimed. Tools Appl. 58(2), 435–452 (2012)

    Article  Google Scholar 

  35. Padilla-Meléndez, A., del Auila-Obra, A.R., Garrido-Moreno, A.: Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Comput. Educ. 63, 306–317 (2013)

    Article  Google Scholar 

  36. Persico, D., Manca, S., Pozzi, F.: Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Comput. Hum. Behav. 30, 614–622 (2014)

    Article  Google Scholar 

  37. Ringle, C.M., Wende, S., Will, A.: SmartPLS 2.0 (beta). Retrieved October 22, 2010 from http://www.smartpls.de (2005)

  38. Rouibah, K., Hamdy, H.I., Al-Enezi, M.Z.: Effect of management support, training, and user involvement on system usage and satisfaction in Kuwait. Ind. Manag. Data Syst. 109(3), 338–356 (2009)

    Article  Google Scholar 

  39. Rutten, N., van Joolingen, W.R., van der Veen, J.T.: The learning effects of computer simulations in science education. Comput. Educ. 58(1), 136–153 (2012)

    Article  Google Scholar 

  40. Smith, J.A., Sivo, S.A.: Predicting continued use of online teacher professional development and the influence of social presence and sociability. Br. J. Educ. Technol. 43(6), 871–882 (2012)

    Article  Google Scholar 

  41. Sun, Y., Zhang, J., Zhang, X.: Critical influence factors for e-learning education system continuance intention. In: Proceedings of the 2011 International Conference on Mechatronic Science, Electric Engineering and Computer. Jilin, China (2011)

  42. Tarhini, A., Hone, K., Liu, X.: User acceptance towards web-based learning systems: investigating the role of social, organizational and individual factors in European higher education. Procedia Comput. Sci. 17, 189–197 (2013)

    Article  Google Scholar 

  43. Teo, T.: Modelling technology acceptance in education: a study of pre-service teachers. Comput. Educ. 52(2), 302–312 (2009)

    Article  Google Scholar 

  44. Teo, T.: Examining the intention to use technology among pre-service teachers: an integration of the technology acceptance model and theory of planned behavior. Interact. Learn. Environ. 20(1), 3–18 (2012)

    Article  Google Scholar 

  45. Tokel, S.T., İsler, V.: Acceptance of virtual worlds as learning space. Innov. Educ. Teach. Int. (in press). doi:10.1080/14703297.2013.820139

  46. Trey, L., Khan, S.: How science students can learn about unobservable phenomena using computer-based analogies. Comput. Educ. 51(2), 519–529 (2008)

    Article  Google Scholar 

  47. van der Meij, J., de Jong, T.: Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learn. Instr. 16(3), 199–212 (2006)

    Article  Google Scholar 

  48. Wassertheurer, S., Leitner, D., Breitenecker, F., Hessinger, M., Holzinger, A.: Parallel computation in blood flow simulation using the lattice-boltzmann method. J. Dev. Trends Model. Simul. 16(2), 64–68 (2006)

    Google Scholar 

  49. Yaman, M., Nerdel, C., Bayrhuber, H.: The effects of instructional support and learner interests when learning using computer simulations. Comput. Educ. 51(4), 1784–1794 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract Nos. NSC 102-2511-S-041-001-, 102-2511-S-041-005-, 101-2511-S-432-001-, and 102-2511-S-432-001-.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong-Ming Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, CH., Huang, YM. An empirical investigation of computer simulation technology acceptance to explore the factors that affect user intention. Univ Access Inf Soc 14, 449–457 (2015). https://doi.org/10.1007/s10209-015-0402-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10209-015-0402-7

Keywords

Navigation