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Examining student decisions to adopt Web 2.0 technologies: theory and empirical tests

  • Richard Hartshorne
  • Haya Ajjan
Article

Abstract

The purpose of this study was to examine student awareness of the pedagogical benefits of Web 2.0 to supplement in-class learning and to better understand factors that influence student decisions to adopt these tools, using the Decomposed Theory of Planned Behavior (DTPB). Findings indicated that while many students feel that some Web 2.0 applications can be effective at increasing satisfaction with a course, improving their learning and their writing ability, and increasing student interaction with other students and faculty; few choose to use them in educational contexts. Additional results indicated that student attitudes and their subjective norms are strong indicators of their intentions to use Web 2.0.

Keywords

Web 2.0 Emerging technologies Student adoption Decomposed theory of planned behavior Factor analysis 

References

  1. Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education, 11(2), 71–80.CrossRefGoogle Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.CrossRefGoogle Scholar
  3. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting behavior. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  4. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147.CrossRefGoogle Scholar
  5. Baylor, A., & Ritchie, D. (2002). What factors facilitate teacher skill, teacher morale, and perceived student learning in technology-using classroom? Computer and Education, 39(1), 395–414.CrossRefGoogle Scholar
  6. Boulos, N. K., & Wheelert, S. (2007). The emerging Web 2.0 social software: An enabling suite of sociable technologies in health and health care education. Health Information and Libraries Journal, 24(1), 2–23.CrossRefGoogle Scholar
  7. Brown, A. L., & Ferrara, R. A. (1985). Diagnosing zones of proximal development. In J. V. Wertsch (Ed.), Culture, communication, and cognition: Vygotskian perspectives (pp. 273–305). NY: Cambridge University Press.Google Scholar
  8. Bruner, J. (1966). Toward a theory of instruction. Cambridge, MA: Harvard University Press.Google Scholar
  9. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211.CrossRefGoogle Scholar
  10. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–339.CrossRefGoogle Scholar
  11. Dearstyne, B. W. (2007). Blogs, mashups, and wikis: Oh my!. Information Management Journal, 41(4), 24–33.Google Scholar
  12. Dixon, S., & Black, L. (1996). Vocal point: A collaborative, student run online newspaper. In E. J. Valauskas & M. Ertel (Eds.), The internet for teachers and school library media specialists: Today’s applications tomorrow’s prospects (pp. 147–158). New York: Neal-Schuman Publishers, Inc.Google Scholar
  13. Ferdig, R. (2007). Examining social software in teacher education. Journal of Technology and Teacher Education, 15(1), 5–10.Google Scholar
  14. Franklin, T., & Van Harmelen, M. (2007). Web 2.0 for content for learning and teaching in higher education. London: Joint Information Systems Committee.Google Scholar
  15. Johnson, R. T., & Johnson, D. W. (1986). Action research: Cooperative learning in the science classroom. Science and Children, 24, 31–32.Google Scholar
  16. Klamma, R., Chatti, M. A., Duval, E., Fiedler, S., Hummel, H., Hvannberg, E. T. et al. (2006). Social software for professional learning: Examples and research issues. Proceedings of the ICALT-2006 Conference, The Netherlands, pp 912–914.Google Scholar
  17. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York: Cambridge University Press.Google Scholar
  18. Lenhart, A., & Madden, M. (2007). Social networking websites and teens: An overview (pp. 1–10). Washington, DC: Pew Internet and American Life Project.Google Scholar
  19. Linn, M. C. (1991). The computer as learning partner: Can computer tools teach science? In K. Sheingold, L. G. Roberts, & S. M. Malcolm (Eds.), Technology for teaching and learning. Washington, DC: American Association for the Advancement of Science.Google Scholar
  20. Madden, M., and Fox, S. (2006). Riding the waves of “Web 2.0”: More than a buzzword, but still not easily defined. Pew Internet Project, 1–6, (Unpublished).Google Scholar
  21. Maloney, E. (2007). What Web 2.0 can teach us about learning? Chronicle of Higher Education, 25(18), B26.Google Scholar
  22. Nunnaly, J. (1978). Psychometric theory. New York: McGraw-Hill.Google Scholar
  23. Pence, H. E. (2007). Preparing for the real web generation. Journal of Educational Technology Systems, 35(3), 347–356.CrossRefGoogle Scholar
  24. Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6.CrossRefGoogle Scholar
  25. Riley, R. W., & Roberts, L. G. (2000). Putting a world-class education at the fingertips of all children: The national educational technology plan. eLearning. Washington, DC: US Department of Education.Google Scholar
  26. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.Google Scholar
  27. Rollett, H., Lux, M., Strohmaier, M., Gisela, D., & Tochtermann, K. (2007). The Web 2.0 way of learning with technologies. International Journal of Learning Technology, 3(1), 87–107.CrossRefGoogle Scholar
  28. Routman, R. (1991). Invitations: Changing as teachers and learners K-12. Toronto, Canada: Irwin Publishing.Google Scholar
  29. Schofield, J. W., & Davidson, A. L. (2002). Bringing the Internet to school: Lessons from an urban district. San Francisco, CA: Jolley-Bass.Google Scholar
  30. 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.CrossRefGoogle Scholar
  31. Simões, L., e Gouveia, L. (2008). Web 2.0 and higher education: Pedagogical implications. Higher Education: New Challenges and Emerging Roles for Human and Social Development. 4th International Barcelona Conference on Higher Education Technical University of Catalonia (UPC).Google Scholar
  32. Sturm, M., Kennel, T., McBride, M., & Kelly, M. (2008). The pedagogical implications of Web 2.0. In M. Thomas (Ed.), Handbook of research on Web 2.0 and second language learning (pp. 367–384). Hershey, PA: IGI Publishing.Google Scholar
  33. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.CrossRefGoogle Scholar
  34. Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28–45.Google Scholar
  35. Triandis, H. C. (1979). Values, attitudes, and interpersonal behavior. In M. M. Page (Ed.), Nebraska symposium on motivation (Vol. 27, pp. 195–259). Lincoln, NE: University of Nebraska Press.Google Scholar
  36. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.Google Scholar
  37. Wright, S. (1921). Correlation and causation. Journal of Agriculture Research, 20, 557–585.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Educational LeadershipUniversity of North Carolina at CharlotteCharlotteUSA
  2. 2.Department of Business Information Systems and Operations ManagementUniversity of North Carolina at CharlotteCharlotteUSA

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