Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms

• Published in: Interactive learning environments Vol. 31; no. 3; pp. 1214 - 1228
• Main Authors: Alshurideh, Muhammad, Al Kurdi, Barween, Salloum, Said A., Arpaci, Ibrahim, Al-Emran, Mostafa
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 03.04.2023; Taylor & Francis Ltd
• Subjects: Algorithms; Artificial Intelligence; Classifiers; College Students; Comparative Analysis; Dependent variables; ECM; Electronic Learning; Expectation; Foreign Countries; higher education; Information Systems; Intention; Least Squares Statistics; Machine learning; machine learning algorithms; Mathematical models; Mobile learning; Modelling; Multivariate statistical analysis; PLS-SEM; Prediction; Satisfaction; Social Influences; Structural equation modeling; Structural Equation Models; Structural models; TAM; Technology Acceptance Model; Technology adoption; Technology Uses in Education; Technology utilization; Usability; United Arab Emirates
• ISSN: 1049-4820; 1744-5191
• DOI: 10.1080/10494820.2020.1826982

Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in analyzing the structural models. To address these limitations, this study extends the technology acceptance model (TAM) with the expectation-confirmation model (ECM) and social influence to predict the actual use of m-learning systems. A comparative approach using the partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms was employed to test the proposed model with data collected from 448 students. The results revealed that both techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases. The employment of a comparative analytical approach is believed to add a significant contribution to the information systems (IS) literature in general, and the m-learning domain in specific.