A Decade of LMS Acceptance and Adoption Research in Sub-Sahara African Higher Education: A Systematic Review of Models, Methodologies, Milestones and Main Challenges

Abstract

A decade has elapsed since the Learning Management System (LMS) technology permeated its way into higher education in Sub-Saharan Africa (SSA), offering new paradigms of both blended and online mode e-learning delivery. Parallel to other continents, the introduction of LMS stimulated acceptance and adoption intentions among stakeholders in higher education. This necessitated research into faculty members' and students' LMS acceptance and adoption intentions. While some research has been conducted in this dimension, the evidential facts are scattered. There is a need to agglomerate these studies to project a better picture of study patterns and results, to be abreast of the current state of the literature and better direct future research. This study sought to bridge the gap by way of a systematic review of previous studies within a decade of LMS acceptance research in SSA, placing them in contextual paradigms of models, methodologies, milestones, subjects, countries, findings and challenges. Results from a systematic review of 31 studies, revealed key determinants of LMS acceptance/adoption to be Attitude and Perceived Usefulness; followed by Performance Expectancy and Perceived Ease of Use; then lastly Social Influence. Major challenges to LMS implementation identified were ICT infrastructure; LMS usage skills and training; LMS system quality, LMS use policy and management support. TAM1 was the dominant model employed and students were the main subject of studies. Moreover, quantitative approach was the preferred design with Regression as the main statistical tool used for data analysis. The study recommended among others that more UTAUT or TAM3 based studies employing mixed method design with instructors as subjects, using structural equation modelling analysis are needed in SSA LMS research. Leadership and top management of higher education institutions should focus more on ICT infrastructure, LMS usage skills/training, LMS quality related issues, support and ICT policy formulation.

Octennial Review (2010-2018) of Literature on M-Learning for Promoting Distributed-Based Medical Education in Sub-Saharan Africa

“…The outcome was because four studies were from Nigeria, and one was from Ghana, while Botswana and South Africa had three studies and two studies respectively. These subregions; southern and western Africa, are home to two of the most economically advanced countries in Africa as corroborated by Okai-Ugbaje, Ardzejewska, and Ahmed (2017) and Bervell and Umar (2017). The authors reported that world bank classification of countries in the region by income placed South Africa as an upper middle-income country while Ghana, Nigeria, Kenya, Tanzania, Uganda, and Botswana as lower-middle-income countries with potentials for further growth (Bervell & Umar, 2017).…”

“…Consequent upon which many African countries are making efforts in widening access and creating opportunities for the expansion of the Higher Education institutions to accommodate these young adults. However, these efforts are ineffective due to the rising number of students with an average of 50% more students per professor in African universities compared to the global average AAI (as cited in Bervell & Umar, 2017).…”

Modeling the Measurements of the Determinants of ICT Fluency and Evolution of Digital Divide Among Students in Developing Countries—East Africa Case Study

“…Referring to the previous study, commonly used methods to find the most influential variables and to study the digital divide are Logistic Regression (LR) and Concentration Index (CI), respectively [13,15,16,58]. This paper follows a similar approach to investigate the determinants of technology fluency among the EA undergraduate students by class year (1st year to 4th year) using an LR based model [11,12].…”

“…This paper follows a similar approach to investigate the determinants of technology fluency among the EA undergraduate students by class year (1st year to 4th year) using an LR based model [11,12]. Moreover, it uses CI to model the evolution of digital inequalities [13]. A combination of LR and CI in the presence of binary variables is known to perform a better analysis of key factors and to explicitly explore the variations of inequalities among individuals [59][60][61][62].…”

“…In regard to ICT fluency and digital divide in EA, few studies have been conducted in the context of higher education, but no study predicted the determinants of technology fluency to understand better the influences of EA regional local characteristics on undergraduate students' technology skills. Most of the studies used, instead, empirical descriptive statistic methods that describe the undergraduate students' barriers and enablers to technology skills [10][11][12][13]. This paper claims that further studies are needed to understand the reality better and it steps forward by developing one model to study the determinants of ICT fluency and the other to explore the evolution of digital inequalities by class year (1st year to 4th year).…”