Why do University Teachers use E-Learning Systems?

Authors

  • Chin Fei Goh Azman Hashim International Business School, Universiti Teknologi Malaysia
  • Puong Koh Hii Azman Hashim International Business School, Universiti Teknologi Malaysia
  • Owee Kowang Tan Azman Hashim International Business School, Universiti Teknologi Malaysia
  • Amran Rasli Azman Hashim International Business School, Universiti Teknologi Malaysia

DOI:

https://doi.org/10.19173/irrodl.v21i2.3720

Keywords:

E-learning, university teachers, behavioural intention, constructivist beliefs, motivation for instrumental use

Abstract

University teachers are the main players when it comes to integrating e-learning systems into higher education institutions. Prior studies have identified four main antecedents that explain teachers’ technology acceptance in the educational context: (a) subjective norms (SN), (b) technological complexity (TC), (c) constructivist beliefs (CB), and (d) motivation for instrumental use (MOT). In this study, we proposed and tested the dual roles of MOT, one as a causal variable and the other as a mediating variable, to explain university teachers’ acceptance of e-learning systems. To test the research model, we collected data from 174 teachers at a large public university in Malaysia using a self-administered survey. Our study shows that MOT mediates the direct effects of SN, TC, and CB on perceived ease of use (PEOU), perceived usefulness (PU), and behavioural intention (BI). This study offers important policy insight for university administrators who seek to enhance acceptance of e-learning systems among university teachers.

Author Biography

Chin Fei Goh, Azman Hashim International Business School, Universiti Teknologi Malaysia

Senior Lecturer, Department of Business Administration, Azman Hashim International Business School

Published

2020-04-08

How to Cite

Goh, C. F., Hii, P. K., Tan, O. K., & Rasli, A. (2020). Why do University Teachers use E-Learning Systems?. The International Review of Research in Open and Distributed Learning, 21(2), 136–155. https://doi.org/10.19173/irrodl.v21i2.3720

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