Determinants of e-learning adoption among students of developing countries

Richard Boateng, Alfred Sekyere Mbrokoh, Lovia Boateng, Prince Kwame Senyo, Eric Ansong

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Purpose – The purpose of this paper is to investigate the determinants of e-learning adoption (ELA) among students in the University of Ghana.

    Design/methodology/approach – A quantitative research approach comprising of a survey of 337 students was adopted. Data were collected using questionnaires designed in conjunction with 13 factors (computer self-efficacy (CSE), perceived ease of use (PEOU), perceived usefulness (PU) and attitude towards use (ATTU)) in the conceptual model of the study. Data analysis was conducted using structural equation modelling.

    Findings
    – The result revealed that PU and ATTU had a direct effect on ELA whilst, PU and PEOU also had a direct relationship on ATTU. Other variables such as CSE and PEOU had an indirect relationship on ELA though they were found to have an insignificant direct relationship on ELA.

    Practical implications
    – The level of significance of each construct identified in the study provide practical guidance to school administrators and instructors as to which factors to pay close attention to when implementing e-learning projects within their respective institutions.

    Originality/value – This study provides insight into ELA from the students’ perspective, through an extension of the TAM model in a developing country context given the existence of cultural differences and societal idiosyncrasies which exist in different contexts, particularly in Africa.
    Original languageEnglish
    Pages (from-to)248-262
    JournalInternational Journal of Information and Learning Technology
    Volume33
    Issue number4
    Early online date21 Jul 2016
    DOIs
    Publication statusPublished - 1 Aug 2016

    Keywords

    • Determinants
    • Ghana
    • E-learning
    • Developing countries
    • Adoption
    • Structural equation modelling (SEM)

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