Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service teachers

Prieto, José Carlos Sánchez; Hernández-García, Ángel; García-Peñalvo, Francisco José; Chaparro-Peláez, Julián; Olmos-Migueláñez, Susana

doi:10.1016/j.chb.2019.01.019

Cited by 95 publications

(74 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, there is some inconsistency when it comes to the relationship between parents' PEOU and their BI to allow their children to use m-learning. Studies [10] and [16] found that PEOU does not necessarily have a direct effect on BI. On the contrary, Alshmrany et al [17] reported that PEOU has a positive direct effect on BI.…”

Section: Literature Reviewmentioning

confidence: 96%

“…However, parents were concerned about the health and social issues associated with overexposure to technology [16]. A plethora of studies has shown that parents' PEOU has a positive effect on their ATT and PU [10,16,17,19,29,39]. However, there is some inconsistency when it comes to the relationship between parents' PEOU and their BI to allow their children to use m-learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze data making use of the software SmartPLS 3. One of the functions of PLS-SEM is the prediction of the target variable [10] in this case rural parents whose children are pursuing STEM's behavioral intention to allow their children to use m-learning. PLS-SEM was also used to assess the predictive power of antecedent variables.…”

Section: Analysis Techniquementioning

confidence: 99%

“…First, the reliability and validity of different model variables were assessed to confirm the quality of the outer model. In the second step, the relationships within the structural model were assessed by testing the significance of the relationships, explained variance of the endogenous variables and predictive power of different variables [10].…”

Section: Analysis Techniquementioning

confidence: 99%

“…However, despite the positive effects m-learning can bring to rural STEM learners and the ubiquity of mobile devices, its adoption into the classroom is far below the expected rate [10]. Davis [11] stated that the successful adoption of any information system contingent on the user's acceptance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Understanding Rural Parents’ Behavioral Intention to Allow Their Children to Use Mobile Learning

Mutambara¹,

Bayaga²

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Faced with many challenges resulting in learners' poor performance at the matriculation level, emphasis on Science, Technology, Engineering and Mathematics (STEM) education is in its infancy in South African's high schools. However, studies have shown that mobile learning (m-learning) can be used to mitigate the challenges of STEM education. Despite, the benefits of mobile learning to rural STEM learners, its full potential has not been realized because the adoption of m-learning depends on users' acceptance. Prior studies focused on teachers' and learners' acceptance of mobile learning. However, little is known about parents' acceptance of m-learning, especially in rural areas. This study explores the acceptance of m-learning by parents of rural high school STEM learners. The study proposes the parents' acceptance of m-learning model, which extends the technology acceptance model by introducing perceived social influence and perceived resources. Stratified random sampling was used to select 200 parents in the survey. Partial least squares structural equation modeling (PSL-SEM) was used to analyze data from 129 valid questionnaires. The proposed model explained 41% of the variance in parents' acceptance of mobile learning. Attitude towards the use was found to be the best predictor and the only factor that have a direct effect on behavioral intention to use mobile learning. However, all other factors have an indirect influence on behavioral intention. The findings revealed that for mobile learning to be successfully implemented in rural areas, resources need to be provided.

show abstract

Section: Literature Reviewmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

Section: Analysis Techniquementioning

confidence: 99%

Section: Analysis Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Understanding Rural Parents’ Behavioral Intention to Allow Their Children to Use Mobile Learning

Mutambara¹,

Bayaga²

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

Examining the predictive relevance of security, privacy risk factors, and institutional logics for e‐government service adoption

Bayaga

2021

E J Info Sys Dev Countries

View full text Add to dashboard Cite

Following the increasing debate around the possible influencers on the adoption of electronic-government (e-government) services, the current research was aimed at examining the predictive relevance of security and privacy risk factors through the lens of institutional logics for e-government service adoption. Guided by the research aim, the study adopted the questionnaire method. Based on the review of the relevant literature and the study's aim, the partial least square structural equation modeling (PLS-SEM) was employed to analyze the data by making use of the SmartPLS 3.2.8 software to assess both the outer and structural models. It was evidenced that privacy and security risk factors as well as institutional logics together account for 61.7% of South African citizens' reasons for adopting e-government services, while the other 38.3% were explained by factors that did not form part of this study. These findings contributed a new view and an extension of the model, providing the insight that privacy and security risk factors as well as institutional logics together are significant predictors of citizens' willingness to adopt e-government services. By implication, the results establish the interconnectedness of the predictive relevance of institutional logics and the adoption of e-government services.

show abstract

Measuring Students’ Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model

Cruz-Benito

Prieto

Therón

et al. 2019

Learning and Collaboration Technologies. Designing Learning Experiences

View full text Add to dashboard Cite

Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service teachers

Cited by 95 publications

References 74 publications

Understanding Rural Parents’ Behavioral Intention to Allow Their Children to Use Mobile Learning

Understanding Rural Parents’ Behavioral Intention to Allow Their Children to Use Mobile Learning

Examining the predictive relevance of security, privacy risk factors, and institutional logics for e‐government service adoption

Measuring Students’ Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model

Contact Info

Product

Resources

About