Skip to main content
SpringerLink
Log in

Factors influencing student acceptance and use of academic portals

  • Published:
Journal of Computing in Higher Education Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting behavior. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Chen, L.-d., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705–719.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chin, W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1).

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

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

    Article  Google Scholar 

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Jöreskog, K. G., & Sörbom, D. (2001). LISREL 8.51. Lincolnwood, IL: Scientific Software International, Inc.

    Google Scholar 

  • Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804.

    Google Scholar 

  • Kendall, J. R. (2005). Implementing the web of student services. New Directions for Student Services, 2005(112), 55–68.

    Article  Google Scholar 

  • 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 

  • Laudon, K., & Traver, C. (2008). E-commerce 2009 (5th ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • 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 

  • Lewis, C. (2002). Driving factors for e-learning: An organisational perspective. Perspectives: Policy and Practice in Higher Education, 6(2), 50–54.

    Article  Google Scholar 

  • Lin, C., Wu, S., & Tsai, S. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42(5), 683–693.

    Article  Google Scholar 

  • 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 

  • Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38(4), 217–230.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ogara, S. O. (2008). Factors influencing email usage: An empirical study: Proceedings of the Decision Sciences Institute Annual Meeting, Baltimore, MD.

  • Pijpers, G. (2003). Guus Pijpers—research. Retrieved January 6, 2009, from http://www.guuspijpers.com/TAM.htm.

  • 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.

    Article  Google Scholar 

  • Rogers, E. M. (2005). Diffusion of innovation (5th ed.). New York, NY: Free Press.

    Google Scholar 

  • Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17(4), 517–525.

    Article  Google Scholar 

  • Stair, R., & Reynolds, G. (2008). Principles of information systems. Boston, MA: Thomson Course Technology.

    Google Scholar 

  • Stunden, A. (2002). New horizons. Educause Review, 37(3), 58.

    Google Scholar 

  • Szajna, B. (1994). Software evaluation and choice: Predictive validation of the technology acceptance instrument. MIS Quarterly, 18(3), 319–324.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • Whitehouse, K. (2006). Cutting through the clutter: What makes an intranet successful? Educause Quarterly, 29(1), 65–69.

    Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business, Truman State University, 100 East Normal, Kirksville, MO, 63501, USA

    Adrien Presley & Theresa Presley

Authors
  1. Adrien Presley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Theresa Presley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrien Presley.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Presley, A., Presley, T. Factors influencing student acceptance and use of academic portals. J Comput High Educ 21, 167–182 (2009). https://doi.org/10.1007/s12528-009-9022-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12528-009-9022-7

Keywords

Search

Navigation