Behavioral Intention to Re-Use Online Learning Platform

  • Akram Harmoni Wiardi(1*)
    University of Bengkyulu
  • Trisna Murni(2)
  • Rina Sutia Hayu(3)
  • Effed Darta Hadi(4)
  • (*) Corresponding Author
Keywords: usefulness, online, intention, platform, acceptance,

Abstract

The Technology Acceptance Model (TAM) is a model used to predict user acceptance of information systems based on perceived usefulness and perceived ease of use. If the user sees the benefits and ease of using the information system, it will cause the user's actions to accept the use of the information system. This study aims to examine the effect of IT system quality and perceived usefulness on the intention to reuse online learning applications. This research analyze the measurement of perceptions of the quality of the IT system used by respondents in the scope of online learning. We operate a survey method, the respondents in this reseacrh were students and teaching staff users of online learning applications. Respondents, most of whom are students in Sumatra, are interested or have the intention of reusing online learning applications if there is a good information quality factor and more benefits from online applications. The perceived usefulness construct plays a full role as a mediating variable of the relationship between information quality and intention to reuse online learning applications.

Downloads

Download data is not yet available.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.
Abdillah, W. (2009), “The Effect Dispositional And Situasional Cognitive Factors On The Intention To Use Internet: An Empirical Study Of The Acceptance Of Information Technology At Universitas Bengkulu,” Journal of Indonesian Economy and Business, Volume 24, Number 2, 2009, 177 – 204.
Davis, Fred D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. September, pp.319- 340.
Davis, Fred D., Bagozzi, Richard P., dan Warshaw, Paul R. (1989). User Acceptance Of Computer Technology: A Comparison Two Theoretical Models Management Science. August.pp.982-1003.
DeLone, W.H. (1988). Determinants of Success for Computer Usage in Small Business. MIS Quarterly/March. Pp. 51-61.
DeLone, W.H., and Ephraim R. Mclean. (1992). Information System Success: The Quest for the Dependent Variable. Information System Research, March. 60-95.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., dan Ronald L. (2010). Multivariate Data Analysis: A Global Perpective (7th Edition). New Jersey : Pearson.
Hertzfeld, E. (2019), “Japan’s Henn na hotel fires half its robot workforce,” Hotel Management, 2020, available at: https://www.hotelmanagement.net/tech/japans-henn-na-hotel-fires-half-its-robot-workforce.
Livari, Juhani. (2005). An Empirical Test of the DeLone and McLean Model ofInformation System Success. Database for Advances in Information Systems. Spring.36,2.pg.8.
Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities: An empirical study. Internet Research, 17(2), 119–138.
Liu, C., and Arnett, K.P. (2000). Exploring the factors associated with website successin the context of electronic commerce. Information and Management,38, 23– 33
Murphy, J., Gretzel, U. and Pesonen, J. (2019), “Marketing robot services in hospitality and tourism: the role of anthropomorphism”, Journal of Travel and Tourism Marketing, Vol. 36 No. 7, pp. 784-795
McHaney, R., and Cronan, T. P. (2001). A Comparison of Surrogate Success Measuresin On-Going Representational Decision Support Systems: An Extension toSimulation Technology. Journal of End User Computing.13, 2.
McKiney, V., Yoon, K., and Zahedi,Fatemeh. (2002). The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach. InformationSystem Research. 133: 296-315.
McGill, Tanya, Hobbs, Valerie, and Klobas, Jane. (2003). User-DevelopedApplications and Information Systems Success: a Test of DeLone and McLean’sModel.Information resource Management Journal; Jan-Mar; 16.1.pg.24.
Rai, A., Lang, S.S. and Welker, R.B., (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information systems research, 13(1), pp.50-69.
Schiffman, L.G., and L.L. Kanuk. (2004). Consumer behavior, 8thinternational ed. Upper Saddle River, NJ: Prentice Hall
Szajna, Bernadette (1996) Empirical Evaluation of the Revised Technology Acceptance Model. Management Science 42(1):85-92. http://dx.doi.org/10.1287/mnsc.42.1.85
Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, pp.425-478.
Zhong, L., Zhang, X., Rong, J., Chan, H.K., Xiao, J. and Kong, H. (2020). Construction and empirical research on acceptance model of service robots applied in hotel industry. Industrial Management & Data Systems.

PlumX Metrics

Published
2022-03-29
How to Cite
Wiardi, A., Murni, T., Hayu, R., & Hadi, E. (2022). Behavioral Intention to Re-Use Online Learning Platform. Journal of Health and Behavioral Science, 4(1), 1-16. https://doi.org/10.35508/jhbs.v4i1.4781
Section
Articles