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Mobile Learning Adoption at a Science Museum

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Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1228))

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Abstract

Funding cuts by the Department for Culture, Media and Sport (DCMS) in the UK has led to Service Level Agreement (SLA) breaches. This is due to an overstretched service desk team. To mitigate this problem, this paper investigates serving just-in-time knowledge in the form of knowledge base articles to service users via mobile learning. Mobile learning (mLearning) could reduce ICT support calls, increase productivity for both service desk staff and the service user. Moreover, it presents an opportunity to develop useful technical knowledge among non-ICT staff. However, challenges are pervasive in any technological adoption. This paper uses the unified theory of acceptance and use of technology (UTAUT) model to explain the determinants of mLearning adoption at a Science Museum (SM). Results indicate that the UTAUT constructs including performance expectancy, effort expectancy, social influence and facilitating conditions are all significant determinants of behavioural intention to use mLearning. A newly proposed construct, self-directed learning was not a significant determinant of behaviour intentions. Further examination found age and gender moderate the relationship between the UTAUT constructs. These findings present several useful implications for mLearning research and practice for ICT service desk at the SM. The research contributes to mLearning technology adoption and strategy.

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Authors and Affiliations

  1. University of Worcester, Worcester, UK

    Ruel Welch, Temitope Alade & Lynn Nichol

Authors
  1. Ruel Welch
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  2. Temitope Alade
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  3. Lynn Nichol
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Corresponding author

Correspondence to Ruel Welch .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Welch, R., Alade, T., Nichol, L. (2020). Mobile Learning Adoption at a Science Museum. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_49

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