Advertisement

Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM)

  • Eddie W. L. ChengEmail author
Research Article
  • 267 Downloads

Abstract

Conflicting perspectives exist regarding the application of the technology acceptance model (TAM) and the theory of planned behavior (TPB) to the study of technology acceptance behavior. The present study addressed the controversy by evaluating and comparing the predictive power of the two theories in a specific context, which was to measure students’ intentions to use a wiki for group work and their behaviors in doing so. A total of 174 students from a university in Hong Kong participated in the study. Three hypothesized models were examined using factor-based partial least squares structural equation modeling (PLS-SEM), which can account for measurement errors and is thus more robust than regression-based PLS-SEM. The results likely rebut the view that the TPB is inferior to the TAM. Moreover, this research highlighted the importance of social influences on collaborative e-learning.

Keywords

Theory of planned behavior Technology acceptance model Factor-based PLS-SEM Consistent PLS-SEM Social influences 

Notes

Acknowledgements

This study was funded by The Education University of Hong Kong (Grant Number: T0148). The author would like to thank the five anonymous reviewers for their insightful comments on earlier drafts of the paper.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

References

  1. Ahmed, E., & Ward, R. (2016). A comparison of competing technology acceptance models to explore personal, academic and professional portfolio acceptance behavior. Journal of Computers in Education, 3(2), 169–191.Google Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.Google Scholar
  3. Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracin, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173–221). Mahwah, NJ: Erlbaum.Google Scholar
  4. Ajzen, I., & Manstead, A. S. R. (2007). Changing health-related behaviors: An approach based on the theory of planned behavior. In K. van den Bos, M. Hewstone, J. de Wit, H. Schut, & M. Stroebe (Eds.), The scope of social psychology: Theory and applications (pp. 43–63). New York: Psychology Press.Google Scholar
  5. Badri, M., Al-Rashedi, A., Yang, G., Mohaidat, J., & Al-Hammadi, A. (2016). Students’ intention to take online courses in high school: A structural equation model of causality and determinants. Education and Information Technologies, 21(2), 471–497.Google Scholar
  6. Bentler, P. M., & Huang, W. (2014). On components, latent variables, PLS and simple methods: Reactions to Rigdon’s rethinking of PLS. Long Range Planning, 47(3), 138–145.Google Scholar
  7. Biasutti, M. (2011). The student experience of a collaborative e-learning university module. Computers and Education, 57(3), 1865–1875.Google Scholar
  8. Bourgonjon, J., Valcke, M., Soetaert, R., & Schellens, T. (2010). Students’ perceptions about the use of video games in the classroom. Computers and Education, 54(4), 1145–1156.Google Scholar
  9. Bourguignon, D., Yzerbyt, V. Y., Teixeira, C. P., & Herman, G. (2015). When does it hurt? Intergroup permeability moderates the link between discrimination and self-esteem. European Journal of Social Psychology, 45(1), 3–9.Google Scholar
  10. Chen, M.-F., & Tung, P.-J. (2010). The moderating effect of perceived lack of facilities on consumers’ recycling intentions. Environment and Behavior, 42(6), 824–844.Google Scholar
  11. Cheng, E. W. L., & Chu, S. K. W. (2016). Students’ online collaborative intention for group projects: Evidence from an extended version of the theory of planned behavior. International Journal of Psychology, 51(4), 296–300.Google Scholar
  12. Cheng, E. W. L., Chu, S. K. W., & Ma, C. S. M. (2016). Tertiary students’ intention to e-collaborate for group projects: Exploring the missing link from an extended theory of planned behavior model. British Journal of Educational Technology, 47(5), 958–969.Google Scholar
  13. Cheon, J., Lee, S., Crooks, S., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers and Education, 59(3), 1054–1064.Google Scholar
  14. Chu, S.-C. (2011). Viral advertising in social media: Participation in Facebook groups and responses among college-aged users. Journal of Interactive Advertising, 12(1), 30–43.Google Scholar
  15. Chu, T.-H., & Chen, Y.-Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers and Education, 92–93, 37–52.Google Scholar
  16. Chu, S. K. W., & Kennedy, D. M. (2011). Using online collaborative tools for groups to co-construct knowledge. Online Information Review, 35(4), 581–597.Google Scholar
  17. Cress, U., & Kimmerle, J. (2008). A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3, 105–122.Google Scholar
  18. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.Google Scholar
  19. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.Google Scholar
  20. Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York: Psychology Press.Google Scholar
  21. Guo, Y., & Barnes, S. (2009). Virtual item purchase behavior in virtual worlds: An exploratory investigation. Electronic Commerce Research, 9(1), 77–96.Google Scholar
  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). Thousand Oaks, CA: Sage Publication, Inc.Google Scholar
  23. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.Google Scholar
  24. Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853–868.Google Scholar
  25. Kock, N. (2011). Using WarpPLS in e-collaboration studies: Descriptive statistics, settings, and key analysis results. International Journal of e-Collaboration, 7(2), 1–18.Google Scholar
  26. Kock, N. (2014). A note on how to conduct a factor-based PLS-SEM analysis. Laredo, TX: ScriptWarp Systems.Google Scholar
  27. Kock, N. (2015a). Common method bias in PLS-SEM: A full collinearity assessment approach. Laredo, TX: ScriptWarp Systems.Google Scholar
  28. Kock, N. (2015b). WarpPLS 5.0 user manual. Laredo, TX: ScriptWarp Systems.Google Scholar
  29. Kock, N. (2015c). Wheat four versus rice consumption and vascular diseases: Evidence from the China Study II data. Cliodynamics, 6(2), 130–146.Google Scholar
  30. Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580.Google Scholar
  31. Lee, J., Cerreto, F. A., & Lee, J. (2010). Theory of planned behavior and teachers’ decisions regarding use of educational technology. Educational Technology and Society, 13(1), 152–164.Google Scholar
  32. Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers and Education, 54(2), 600–610.Google Scholar
  33. Ma, W. W.-K., & Yuen, A. H. K. (2005). Comparing four competing models in e-learning system acceptance. In K. P. Mehdi (Ed.), Managing modern organizations through information technology (pp. 568–571). Hershey, PA: Information Resources Management Association.Google Scholar
  34. Mak, B., & Coniam, D. (2008). Using wikis to enhance and develop writing skills among secondary school students in Hong Kong. System, 36(3), 437–455.Google Scholar
  35. Marler, J. H., Fisher, S. L., & Ke, W. (2009). Employee self-service technology acceptance: A comparison of pre-implementation and post-implementation relationships. Personnel Psychology, 62(2), 327–358.Google Scholar
  36. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.Google Scholar
  37. Naismith, L., Lee, B.-H., & Pilkington, R. M. (2011). Collaborative learning with a wiki: Differences in perceived usefulness in two contexts of use. Journal of Computer Assisted Learning, 27(3), 228–242.Google Scholar
  38. Ndubisi, N. (2006). Factors of online learning adoption: A comparative juxtaposition of the theory of planned behavior and the technology acceptance model. International Journal on e-Learning, 5(4), 571–591.Google Scholar
  39. Onwezen, M. C., Bartels, J., & Antonides, G. (2014). The self-regulatory function of anticipated pride and guilt in a sustainable and healthy consumption context. European Journal of Social Psychology, 44(1), 53–68.Google Scholar
  40. Rosenberg, M. (1965). Society and adolescent self-image. Princeton, NJ: Princeton University Press.Google Scholar
  41. Rosenberg, M., & Kaplan, H. B. (Eds.). (1982). Social psychology and the self-concept. Arlington Heights, IL: Harlan Davidson.Google Scholar
  42. Sánchez, R. A., Hueros, A. D., & Ordaz, M. G. (2013). E-learning and the University of Huelva: A study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135–160.Google Scholar
  43. Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the emancipation of PLS-SEM: A commentary on Rigdon (2012). Long Range Planning, 47(3), 154–160.Google Scholar
  44. Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103.Google Scholar
  45. Slagter van Tryon, P., & Bishop, M. J. (2009). Theoretical foundations for enhancing social connectedness in online learning environments. Distance Education, 30(3), 291–315.Google Scholar
  46. Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. SAGE Open.  https://doi.org/10.1177/2158244013503837.Google Scholar
  47. Taylor, S., & Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144–176.Google Scholar
  48. 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.Google Scholar
  49. 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, 128–143.Google Scholar
  50. Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers and Education, 57(2), 1645–1653.Google Scholar
  51. Testa, M., & Major, B. (1990). The impact of social comparisons after failure: The moderating effects of perceived control. Basic and Applied Social Psychology, 11(2), 205–218.Google Scholar
  52. 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.Google Scholar
  53. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.Google Scholar
  54. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.Google Scholar
  55. White, K. S., Brown, T. A., Somers, T., & Barlow, D. H. (2006). Avoidance behavior in panic disorder: The moderating influence of perceived control. Behavior Research and Therapy, 44(1), 147–157.Google Scholar
  56. White, K. M., Smith, J. R., Terry, D. J., Greenslade, J. H., & McKimmie, B. M. (2009). Social influence in the theory of planned behaviour: The role of descriptive, injunctive, and in-group norms. British Journal of Social Psychology, 48, 135–158.Google Scholar
  57. Wojciechowski, R., & Cellary, W. (2013). Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Computers and Education, 68, 570–585.Google Scholar
  58. Woo, M., Chu, S., Ho, A., & Li, X. (2011). Using a Wiki to scaffold primary-school students’ collaborative writing. Educational Technology and Society, 14, 43–54.Google Scholar
  59. Yayla, A., & Hu, Q. (2007). User acceptance of e-commerce technology: A meta-analytic comparison of competing models. In Proceedings of the 15th European conference on information system (ECIS) (pp. 179–190), September 10–14, Switzerland.Google Scholar

Copyright information

© Association for Educational Communications and Technology 2018

Authors and Affiliations

  1. 1.Department of Social SciencesThe Education University of Hong KongTai PoHong Kong

Personalised recommendations