Perceived fit and satisfaction on online learning performance: An empirical study

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationEdutainment Technologies
Subtitle of host publicationEducational Games and Virtual Reality/Augmented Reality Applications - 6th International Conference on E-learning and Games, Edutainment 2011, Proceedings
Pages139-146
Number of pages8
DOIs
Publication statusPublished - 2011 Sep 26
Event6th International Conference on E-Learning and Games, Edutainment 2011 - Taipei, Taiwan
Duration: 2011 Sep 72011 Sep 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6872 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on E-Learning and Games, Edutainment 2011
CountryTaiwan
CityTaipei
Period11-09-0711-09-09

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lin, W. S. (2011). Perceived fit and satisfaction on online learning performance: An empirical study. In Edutainment Technologies: Educational Games and Virtual Reality/Augmented Reality Applications - 6th International Conference on E-learning and Games, Edutainment 2011, Proceedings (pp. 139-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6872 LNCS). https://doi.org/10.1007/978-3-642-23456-9_26