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Neural network approach to predict mobile learning acceptance

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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.

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Authors and Affiliations

  1. Department of Information Systems, Sultan Qaboos University, Muscat, Oman

    Hafedh Al-Shihi

  2. Department of Operations Management and Business Statistics, Sultan Qaboos University, Muscat, Oman

    Sujeet Kumar Sharma

  3. Communication and Information Research Center, Sultan Qaboos University, Muscat, Oman

    Mohamed Sarrab

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  1. Hafedh Al-Shihi
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  2. Sujeet Kumar Sharma
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  3. Mohamed Sarrab
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Correspondence to Sujeet Kumar Sharma.

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Table 5 M-learning acceptance measures (MLAM)
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Al-Shihi, H., Sharma, S.K. & Sarrab, M. Neural network approach to predict mobile learning acceptance. Educ Inf Technol 23, 1805–1824 (2018). https://doi.org/10.1007/s10639-018-9691-9

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