Empirical investigation of e-learning acceptance and assimilation: A structural equation model

By Said S. Al-gahtani. 2016

478 citations recorded by the source corpus.

Abstract E-learning has become progressively more vital for academia and corporate training and has potentially become one of the most significant developments and applications in Information Technologies (ITs). This study used a quantitative approach seeking a causative explanation of the decision behavior of individuals toward the acceptance and assimilation of e-learning in academic settings. A survey of 286 participants (students) was conducted to collect the research data. Our study framework was based on the third version of the Technology Acceptance Model (i.e., TAM3) and the data were analyzed using structural equation modeling in order to determine the factors that influence the learners’ intention to use e-learning. Results show the predicting (promoting/inhibiting) factors of e-learning technology acceptance, while also examining some related post-implementation interventions expected to contribute to the acceptance and assimilation of e-learning systems. Our results also indicate that TAM3 holds well in the Arabian culture and also outline valuable outcomes such as: managerial interventions and controls for better organizational e-learning management that can lead to greater acceptance and effective utilization. Hopefully, this study provides a roadmap to more understanding of the success factors and post-implementation interventions contributing to the acceptance and assimilation of e-learning systems in developing countries.

Subfields: Artificial Intelligence, Communication, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/e3e056b9739fd32afa421894a9db7875f61dd912

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Communication

In Information Systems and Management

Citations in the corpus

Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios (2016, background): “…has confirmed that TAM is a valid model that represents an important theoretical framework to explain and predict technology acceptance behaviour (Al-Gahtani, 2016; Hidayanto, Febriawan, Sucahyo & Purwandari, 2014; Hsia, Chang & Tseng, 2014; Lee, Hsiao & Purnomo, 2014; Tarhini, Hone & Liu, 2014;…”
Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model (2017, background): “Factors, such as users ’ perception of technology, as well as their knowledge and skills in using computers, have been shown to affect acceptance and usage (Al-Gahtani, 2014; Tarhini, Hone, & Liu, 2015; Wong, Teo, & Goh, 2015).”
Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) (2017, methodology): “Many research studies have highlighted the important role that PE plays on BI to use webbased learning tools (Al-Gahtani 2016; Chiu and Wang 2008; Chu and Chen 2016; Marchewka et al. 2014; Merhi 2015; Oye et al. 2014).”
Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors (2016, background): “Al-Gahtani (2014) E-learning system Students 286 YES -0.105 p 0.05 Agudo-Peregrina, HernándezGarcía and Pascual-Miguel (2014) Lifelong learning Setting E-learning system Students 81 YES -0.270 p 0.001 Agudo-Peregrina, HernándezGarcía and Pascual-Miguel (2014) Higher education Setting E-learning…”
Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic (2020, background): “Al-Gahtani (2016) investigated the factors influencing student acceptance of e-learning based on TAM3.”
Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model (2021, background): “With regard to PU, previous studies have shown that perceived usefulness (PU) directly affected usage intention of e-learning (Al-Gahtani, 2016; Elkaseh et al., 2016; Hsia et al., 2014; Lee et al., 2014; Tarhini et al., 2014, 2016).”
Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model (2019, methodology): “in previous research that PE has a significant effect on perceived ease of use [48], [53]–[56] and perceived usefulness [14], [57]–[59] of e-learning.”