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Factors influencing student acceptance and use of academic portals

  • Adrien Presley
  • Theresa Presley
Article
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Abstract

Institutions of higher education have increasing turned to web portals as a way to connect with students. These portals are designed to provide students a centralized point of access to information and services. In spite of the efforts put into developing and maintaining these portals, their use by students can be disappointing. The study described in this paper examines factors influencing the acceptance and use of academic intranet portals by university students. The research uses as its theoretical basis the technology acceptance model (TAM), one of the most widely accepted information technology utilization models from information systems literature. Two additional constructs, compatibility and enjoyment were added to the model to determine if an expanded model would better characterize user acceptance and use. A survey administered to 709 university students was analyzed using confirmatory factor analysis and structural equation modeling. The data indicated that ease of use, perceived usefulness, and attitude from the basic TAM model all contributed significantly to explaining intention and usage of the portal. In addition, the integration of enjoyment as a construct was found to improve the fit of the model. Weak measurement properties precluded the analysis of the compatibility construct. The paper includes discussion of practical and academic implications of the research.

Keywords

Technology acceptance model Intranet portals Technology adoption 
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References

  1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247.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. Bagozzi, R. P., & Heatherton, T. F. (1994). A general approach to representing multifaceted personality constructs: Application to state self-esteem. Structural Equation Modeling, 1(1), 35–67.CrossRefGoogle Scholar
  5. Chang, S.-C., & Tung, F.-C. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83.Google Scholar
  6. Chen, L.-d., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705–719.CrossRefGoogle Scholar
  7. Cheung, E. Y. M., & Sachs, J. (2006). Test of the technology acceptance model for a web-based information system in a Hong Kong Chinese sample. Psychological Reports, 99(3), 691–703.CrossRefGoogle Scholar
  8. Chin, W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1).Google Scholar
  9. 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.CrossRefGoogle Scholar
  10. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.CrossRefGoogle Scholar
  11. Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9–21.CrossRefGoogle Scholar
  12. Fornell, C., & Larcker, D. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  13. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.CrossRefGoogle Scholar
  14. Hair, J. F., Black, W. C., Babin, B., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). New Jersey: Prentice Hall.Google Scholar
  15. Hendrickson, A. R., Massey, P. D., & Cronan, T. P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17(2), 227–230.CrossRefGoogle Scholar
  16. Jöreskog, K. G., & Sörbom, D. (2001). LISREL 8.51. Lincolnwood, IL: Scientific Software International, Inc.Google Scholar
  17. Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804.Google Scholar
  18. Kendall, J. R. (2005). Implementing the web of student services. New Directions for Student Services, 2005(112), 55–68.CrossRefGoogle Scholar
  19. Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning and Performance Journal, 22(1), 35–48.Google Scholar
  20. Laudon, K., & Traver, C. (2008). E-commerce 2009 (5th ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  21. Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & management, 42(8), 1085–1104.Google Scholar
  22. Lewis, C. (2002). Driving factors for e-learning: An organisational perspective. Perspectives: Policy and Practice in Higher Education, 6(2), 50–54.CrossRefGoogle Scholar
  23. Lin, C., Wu, S., & Tsai, S. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42(5), 683–693.CrossRefGoogle Scholar
  24. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.CrossRefGoogle Scholar
  25. Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38(4), 217–230.CrossRefGoogle Scholar
  26. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.CrossRefGoogle Scholar
  27. Ogara, S. O. (2008). Factors influencing email usage: An empirical study: Proceedings of the Decision Sciences Institute Annual Meeting, Baltimore, MD.Google Scholar
  28. Pijpers, G. (2003). Guus Pijpers—research. Retrieved January 6, 2009, from http://www.guuspijpers.com/TAM.htm.
  29. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.CrossRefGoogle Scholar
  30. Rogers, E. M. (2005). Diffusion of innovation (5th ed.). New York, NY: Free Press.Google Scholar
  31. Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17(4), 517–525.CrossRefGoogle Scholar
  32. Stair, R., & Reynolds, G. (2008). Principles of information systems. Boston, MA: Thomson Course Technology.Google Scholar
  33. Stunden, A. (2002). New horizons. Educause Review, 37(3), 58.Google Scholar
  34. Szajna, B. (1994). Software evaluation and choice: Predictive validation of the technology acceptance instrument. MIS Quarterly, 18(3), 319–324.CrossRefGoogle Scholar
  35. Tan, W. P., and Chan, H. C. (1998). A TAM-based assessment of videoconferencing for remote tutoring: Proceedings of Association of Information Systems Conference, Baltimore, MD.Google Scholar
  36. Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25–37.CrossRefGoogle Scholar
  37. 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
  38. Whitehouse, K. (2006). Cutting through the clutter: What makes an intranet successful? Educause Quarterly, 29(1), 65–69.Google Scholar
  39. Yi, M. Y., & Hwang, Y. (2003). Predicting the use of eWeb-based information systems: Self-efficacy, enjoyment, learning goal orientation, and technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of BusinessTruman State UniversityKirksvilleUSA

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