Abstract
Online learning systems (OLSs) have been widely implemented by higher education institutions to support teaching and learning by assisting instructors’ and students’ interactive communications. This paper integrates information system (IS) continuance theory with task-technology fit (TTF) to extend understandings of the antecedents of the intention to continue OLS and impacts on learning. Results reveal that perceived fit and satisfaction are important antecedents of the intention to continue OLS and individual performance.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akyol, Z., Garrison, D.R.: Understanding cognitive presence in an online and blended community of inquiry: Assessing outcomes and processes for deep approaches to learning. Br. J. Educ. Tech. 42(2), 233–250 (2010)
Anderson, J.C., Gerbing, D.W.: Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bull. 103(3), 411–423 (1988)
Barclay, D., Higgins, C., Thompson, R.: The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration. Tech. Stud. 2(2), 285–324 (1995)
Bhattacherjee, A.: Understanding information system continuance: An expectation-confirmation model. MIS Q. 25(3), 351–370 (2001)
Bhattacherjee, A., Perols, J., Sanford, C.: Information technology continuance: A theoretic extension and empirical test. J. Comput. Inform. Syst. 49(1), 267–293 (2008)
Bliuc, A., Ellis, R., Goodyear, P., Piggott, L.: Learning through face-to-face and online discussions: Associations between students’ conceptions, approaches and academic performance in political science. Brit. J. Educ.al Tech. 41(3), 512–524 (2010)
Chang, H.H.: Task-Tech. fit and user acceptance of online auction. Int. J. Hum.-Comput. Stud. 68(1-2), 69–89 (2010)
Chang, H.L., Wang, K., Chiu, I.: Business-IT fit in e-procurement system: Evidence from high-technology firms in China. Inform. Syst. J. 18(4), 381–404 (2008)
Cheng, Y.M.: Antecedents and consequences of e-learning acceptance. Inform. Syst. J. 21(3), 269–299 (2010)
Cheung, M.Y., Luo, C., Sia, C.L., Chen, H.: Credibility of Electronic Word-of-Mouth: Informal and Normative Determinants of On-line Consumer Recommendations. Int. J. of Electron. Commerce 13(4), 9–38 (2009)
Chin, W.W., Marcolin, B.L., Newsted, P.R.: A partial least squares latent variable modeling approach for measuring interaction effects: Results form a Monte Carlo simulation study and electronic-mail emotion/adoption study. Inform. Syst. Res. 14(2), 189–217 (2003)
Chiu, C.M., Wang, E.T.G.: Understanding web-based learning continuance intention: The role of subjective task value. Inform. Manage. 45(3), 194–201 (2008)
Eijl, P.V., Pilot, A., Voogd, P.: Effects of collaborative and individual learning in a blended learning environment. Educ. Inform. Technolog. 10(1/2), 49–63 (2005)
Goodhue, D., Thompson, R.L.: Task-Tech. fit and individual performance. MIS Q. 19(2), 213–236 (1995)
Hui, W., Hu, P.J., Clark, T.H.K., Tam, K.Y., Milton, J.: Technology-assisted learning: a longitudinal field study of knowledge category, learning effectiveness and satisfaction in language learning. J. of Comput. Assisted Learn. 24(3), 245–259 (2008)
Hu, P., Brown, S.A., Thong, J., Chan, F., Tam, K.Y.: Determinants of services quality and continuance intention of online services: The case of eTax. J. of the AM. Soc. for Inform. Sci. Tech. 60(2), 292–306 (2009)
Larsen, T., Sørebø, A.M., Sørebø, Ø.: The role of task-technology fit as users’ motivation to continue information system use. Comput. Hum. Behav. 25(3), 778–784 (2009)
Limayem, M., Hirt, G., Cheung, C.M.K.: How habit limits the predictive power of intention: The case of information systems continuance. MIS Q. 31(4), 705–737 (2007)
Karahanna, E., Agarwal, R., Angst, C.M.: Reconceptualizing compatibility beliefs in technology acceptance research. MIS Q. 30(4), 781–804 (2006)
Komiak, S.Y.X., Benbasat, I.: The effects of personalization and familiarity on trust in and adoption of recommendation agents. Manage. Inform. Syst. Q. 30(4), 941–960 (2006)
Lim, D.H., Morris, M.L.: Learner and instructional factors influencing learning outcomes within a blended learning environment. Educ.al Tech. & Soc. 12(4), 282–293 (2009)
Liao, C., Palvia, P., Chen, J.L.: Information Technology adoption behavior life cycle: Toward a technology continuance theory. Int. J. of Inform. Manage. 29(4), 309–320 (2009)
Limayem, M., Cheung, C.: Understanding information systems continuance: The case of Internet-based learning technologies. Inform. Manage. 45(4), 227–232 (2008)
Lin, K.M.: E-learning continuance intention: Moderating effect of user e-learning experience. Comput. & Educ. 56(2), 515–526 (2011)
Lu, J., Yu, C.S., Liu, C.: Learning style, learning patterns, and learning performance in a WebCT-based MIS course. Inform. Manage. 40(6), 497–507 (2003)
McGill, T.J., Hobbs, V.J.: How students and instructors using a virtual learning environment perceive the fit between technology and task. J. Comput. Assisted Learn. 24(3), 191–202 (2008)
Roca, J.C., Gagné, M.: Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Comput. Hum. Behav. 24(4), 1585–1604 (2008)
Sørebø, Ø., Halvari, H., Gulli, V.F., Kristiansen, R.: The role of self-determination theory in explaining teacher’s motivation to continue to use e-learning technology. Comput. Educ. 53(4), 1177–1187 (2009)
Yen, D., Wu, C.S., Cheng, F.F., Huang, Y.W.: Determinants of users’ intention to adopt wireless Tech.: An empirical study by integrating TTF with TAM. Comput. Hum. Behav. 26(5), 906–915 (2010)
Yeung, P., Jordan, E.: The continued usage of business e-learning course in Hong Kong corporations. Educ. Inform. Tech. 12(3), 175–188 (2007)
Wan, Z., Wang, Y., Haggerty, N.: Why people benefit from e-learning differently: The effects of psychological processes on e-learning outcomes. Inform. Manage. 45(8), 513–521 (2008)
Wu, C.G., Gerlach, J.H., Young, C.E.: An empirical analysis of open source software developers’ motivations and continuance intentions. Inform. Manage. 44(3), 253–262 (2007)
Zhou, T., Lu, Y., Wang, B.: Integrating TTF and UTAUT to explain mobile banking user adoption. Comput. Hum. Behav. 26(4), 760–767 (2010)
Zigurs, I., Buckland, B.K.: A theory of task-technology fit and group support systems effectiveness. MIS Q. 22(3), 313–334 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, WS. (2011). Perceived Fit and Satisfaction on Online Learning Performance: An Empirical Study. In: Chang, M., Hwang, WY., Chen, MP., Müller, W. (eds) Edutainment Technologies. Educational Games and Virtual Reality/Augmented Reality Applications. Edutainment 2011. Lecture Notes in Computer Science, vol 6872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23456-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-23456-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23455-2
Online ISBN: 978-3-642-23456-9
eBook Packages: Computer ScienceComputer Science (R0)