Neural network approach to predict mobile learning acceptance

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10 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

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Keywords

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

ASJC Scopus subject areas

  • Education
  • Library and Information Sciences

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