Investigating Factors M-Learning Acceptance and Use for Distance Learning Students in Higher Education

Abstract

Many research has been conducted to examine the acceptance factors to use mobile learning (m-learning) for regular students. During the COVID-19 most of the higher education institutions around the world were converted to m-learning especially for regular students, in order to continue supporting the educational stage for these students. This situation, allow researches to tested the use of m-learning for regular students while they are studying in distance learning environment. However, limited researches, especially in developing countries, have been tested the acceptance factors to use m-leaning for distance learning students. In this study the behavioral intention to use mobile learning (m-learning) were examined as well as the m-learning factors that affecting its acceptance amongst the distance learning students were outlined. The study framework was depended on the model of Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative approach was used to analyze the data that collected from a random sample of 154 male and female participants from Saudi universities. The results indicated that significant factors influencing distance learning students’ behavioral intention include quality of service, effort expectancy, facilitating conditions, gender, educational level, and type of device. The regulations governing distance learning programs and the implementation of mobile learning by Saudi universities under the direction of the Ministry of Higher Education are having a good impact and encouraging widespread use of m-learning.

Teaching digital competence in the use of YouTube and its incidental factors: Development of an instrument based on the UTAUT model from a higher order PLS‐SEM approach

“…Thus, increasing one's sense of the ease of use of YouTube stands in relation to an internal positive attitude towards the use of this type of esolution. This relationship is known from other reports relating to the implementation of new technologies in education (Al Masarweh & Afandi, 2022;Scherer et al, 2019Scherer et al, , 2020. The present hypothesis H1 is therefore not only confirmed (statistically significant) by the results of the present study, but also by the other cited studies.…”

Continued Usage Intention of Mobile Learning (M-Learning) in Iraqi Universities Under an Unstable Environment: Integrating the ECM and UTAUT2 Models

“…Consistent with the UTAUT2 and previous research, results show that antecedents such as CC, EE, SI, and HB substantially influence CUI. This lends credence to hypotheses H1, H3, H4, and H7 (Almaiah & Alismaiel, 2019;Chao, 2019;Lutfi et al, 2022;Waleed, 2022). In addition, CON, IDQ, and CTQ have an indirect effect on continued usage intention of m-learning through user SAT, as well as the direct effect of SAT on CUI, which supports hypotheses H8, H9, H10 and H11 (Alshurideh et al, 2020;Alzaidi & Shehawy, 2022;Roslan et al, 2023).…”

“…With a path coefficient of 0.540, which is significant at the 0.001 level, results identified user SAT as the most important driver of CUI, validating hypotheses. Students' CUI to use m-learning was found to be directly affected by SAT (Waleed, 2022).…”