Approach to M-learning Acceptance Among University Students

Gómez-Ramirez, Isabel; Valencia-Arias, Alejandro; Cano, Laura Duque

doi:10.19173/irrodl.v20i4.4061

Cited by 90 publications

(81 citation statements)

References 40 publications

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“…SN, which is one of the theories of planned behavior variables developed by [18], is defined as PSETs beliefs that their important people would support the use of m-LMS. Findings from previous studies informed that SN has a significant relationship with PU of Web 2.0 use [27] and behavioral intention to use m-learning [40]. H7: SN is significantly predicting PU H8: SN is significantly predicting BI.…”

Section: H Subjective Norms (Sn)mentioning

confidence: 84%

Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

et al. 2020

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This study aimed at reporting the drivers predicting Pre-Service English Teachers' (PSETs) behavioral intention to use m-learning Management Systems (m-LMS) in their learning activities. To achieve the purpose of the study, an extended technology acceptance model (TAM) was administered. Six drivers were included to examine 11 paths of the study framework. The instrument was adapted from previous studies and validated through content validity. Further, it was piloted to 76 PSETs for the reliability. Two hundred and ten responses were analyzed for the main study. The results of the study were obtained by implementing the procedures of Partial Least Squares Structural Equation Modeling (PLS-SEM). The extended TAM-based drivers are reported to be valid and reliable in the measurement model stage. In the assessment of the structural model, nine out of the eleven paths are significant. The strongest significant relationship is reported to emerge between perceive usefulness and attitude toward m-LMS while the weakest significant relationship is reported between self efficacy and perceived usefulness. On the other hand, the two paths that are not positively correlated are between; 1) supporting condition and perceived usefulness; 2) subjective norm and use of m-LMS. Recommendations for future research regarding the behavioral intention to use m-LMS in developing countries are provided.

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Section: H Subjective Norms (Sn)mentioning

confidence: 84%

Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

et al. 2020

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“…TAM model has been used by many researchers recently to measure the impact of technology acceptance and adoption [34][35][36]. The two constructs within the TAM model, namely the perceived ease of use and perceived usefulness are crucial factors in measuring the effect of technology on its users within the educational sector [36][37][38][39][40].…”

Section: Tam Modelmentioning

confidence: 99%

Acceptance of Google Meet during the Spread of Coronavirus by Arab University Students

et al. 2021

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The COVID-19 pandemic not only affected our health and medical systems but also has created large disruption of education systems at school and universities levels. According to the United Nation’s report, COVID-19 has influenced more than 1.6 billion learners from all over the world (190 countries or more). To tackle this problem, universities and colleges have implemented various technologically based platforms to replace the physical classrooms during the spread of Coronavirus. The effectiveness of these technologies and their educational impact on the educational sector has been the concern of researchers during the spread of the pandemic. Consequently, the current study is an attempt to explore the effect of Google Meet acceptance among Arab students during the pandemic in Oman, UAE, and Jordan. The perceived fear factor is integrated into a hybrid model that combines crucial factors in TAM (Technology acceptance Model) and VAM (Value-based Adoption Model). The integration embraces perceived fear factor with other important factors in TAM perceived ease of use (PEOU) and perceived usefulness (PU) on the one hand and technically influential factor of VAM, which are perceived technicality (PTE) and perceived enjoyment (PE) on the other hand. The data, collected from 475 participants (49% males and 51% females students), were analyzed using the partial least squares-structural equation modelling (PLS-SEM). The results have shown that TAM hypotheses of usefulness and easy to use have been supported. Similarly, the results have supported the hypotheses related to VAM factors of being technically useful and enjoying, which helps in reducing the atmosphere of fear that is created due to the spread of Coronavirus.

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“…UTAUT was used as a theoretical framework in this study to understand learners' perceptions regarding mlearning acceptance. UTAUT has attracted significant attention in m-learning research (Aliaño et al, 2019;Gómez-Ramirez et al, 2019;Nikou & Economides, 2017). However, the ability of this model to predict technology acceptance is not high (Mtebe et al, 2016), suggesting that other factors should be integrated to improve its overall predictability power.…”

Section: The Proposed Research Modelmentioning

confidence: 99%

“…Therefore, the acceptance of m-learning receives considerable attention as it is still an evolving topic (Briz-Ponce et al, 2017;Nikou & Economides, 2017). In particular, the adoption of m-learning has been examined based on the perceptions of students and teachers (Al-Emran et al, 2016;Aliaño, et al, 2019;Althunibat, 2015;Gómez-Ramirez et al, 2019;Nikou & Economides, 2017;Thomas et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Predicting mobile learning acceptance: An integrated model and empirical study based on higher education students' perceptions

Alowayr

Al-Azawei

2021

AJET

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This research extends the Unified Theory of Acceptance and Use of Technology (UTAUT) model based on expectation-confirmation and self-determination theories. It aims at exploring students' perspectives regarding the acceptance of mobile learning (m-Learning) in higher education. Although UTAUT receives considerable attention in technology acceptance research, this study unlike previous work as it is among the first to integrate self-determination and expectation-confirmation theories with this model in understanding m-Learning adoption, particularly in a case of developing countries. A total of 246 undergraduate students responded voluntarily to an online questionnaire. The findings suggest that integrating the UTAUT model with variables represent learners' basic psychological needs can highly affect their acceptance of m-Learning technology. The findings are further being discussed for theoretical and practical implications.

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Approach to M-learning Acceptance Among University Students

Cited by 90 publications

References 40 publications

Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

Acceptance of Google Meet during the Spread of Coronavirus by Arab University Students

Predicting mobile learning acceptance: An integrated model and empirical study based on higher education students' perceptions

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