Neural network approach to predict mobile learning acceptance

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalEducation and Information Technologies
DOIs
Publication statusAccepted/In press - Feb 23 2018

Fingerprint

neural network
acceptance
learning
social learning
flexibility
Oman
efficiency
capital city
proliferation
service provider
telecommunication
economics
student
developing country

Keywords

  • M-learning
  • Mobile technologies
  • Neural network
  • Oman
  • Technology adoption

ASJC Scopus subject areas

  • Education
  • Library and Information Sciences

Cite this

Neural network approach to predict mobile learning acceptance. / Al-Shihi, Hafedh; Sharma, Sujeet Kumar; Sarrab, Mohamed.

In: Education and Information Technologies, 23.02.2018, p. 1-20.

Research output: Contribution to journalArticle

@article{2231487e5c8e4e60bd237a7d69546cee,
title = "Neural network approach to predict mobile learning acceptance",
abstract = "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.",
keywords = "M-learning, Mobile technologies, Neural network, Oman, Technology adoption",
author = "Hafedh Al-Shihi and Sharma, {Sujeet Kumar} and Mohamed Sarrab",
year = "2018",
month = "2",
day = "23",
doi = "10.1007/s10639-018-9691-9",
language = "English",
pages = "1--20",
journal = "Education and Information Technologies",
issn = "1360-2357",
publisher = "Kluwer Academic Publishers",

}

TY - JOUR

T1 - Neural network approach to predict mobile learning acceptance

AU - Al-Shihi, Hafedh

AU - Sharma, Sujeet Kumar

AU - Sarrab, Mohamed

PY - 2018/2/23

Y1 - 2018/2/23

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

UR - http://www.scopus.com/inward/record.url?scp=85045036005&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045036005&partnerID=8YFLogxK

U2 - 10.1007/s10639-018-9691-9

DO - 10.1007/s10639-018-9691-9

M3 - Article

AN - SCOPUS:85045036005

SP - 1

EP - 20

JO - Education and Information Technologies

JF - Education and Information Technologies

SN - 1360-2357

ER -