TY - JOUR
T1 - Neural network approach to predict mobile learning acceptance
AU - Al-Shihi, Hafedh
AU - Sharma, Sujeet Kumar
AU - Sarrab, Mohamed
N1 - Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - The proliferation of mobile computing technologies is playing major role in the growth of mobile learning (M-learning) market around the globe. The purpose of this paper is to develop a research model in the lines of commonly used models the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM) by incorporating constructs namely flexibility learning, social learning, efficiency learning, enjoyment learning, suitability learning, and economic learning that can predict M-learning adoption in a developing country. The data were collected from 388 students from all major universities/colleges in the capital city (Muscat) of Oman. The neural network modeling was employed to predict M-learning adoption. The neural network modeling results showed that flexibility learning, social learning, efficiency learning, enjoyment learning, suitability learning, and economic learning variables have significant influence on the intention of students to accept mobile learning. The key outcomes of this study suggest important determinants that can assist academic administrators and telecommunication service providers to enhance the adoption of M-learning with the help of suitable strategy.
AB - The proliferation of mobile computing technologies is playing major role in the growth of mobile learning (M-learning) market around the globe. The purpose of this paper is to develop a research model in the lines of commonly used models the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM) by incorporating constructs namely flexibility learning, social learning, efficiency learning, enjoyment learning, suitability learning, and economic learning that can predict M-learning adoption in a developing country. The data were collected from 388 students from all major universities/colleges in the capital city (Muscat) of Oman. The neural network modeling was employed to predict M-learning adoption. The neural network modeling results showed that flexibility learning, social learning, efficiency learning, enjoyment learning, suitability learning, and economic learning variables have significant influence on the intention of students to accept mobile learning. The key outcomes of this study suggest important determinants that can assist academic administrators and telecommunication service providers to enhance the adoption of M-learning with the help of suitable strategy.
KW - M-learning
KW - Mobile technologies
KW - Neural network
KW - Oman
KW - Technology adoption
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U2 - 10.1007/s10639-018-9691-9
DO - 10.1007/s10639-018-9691-9
M3 - Article
AN - SCOPUS:85045036005
SN - 1360-2357
VL - 23
SP - 1805
EP - 1824
JO - Education and Information Technologies
JF - Education and Information Technologies
IS - 5
ER -