Mobile Learning Adoption at a Science Museum

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Welch, Ruel, Alade, Temitope and Nichol, Lynn (2020) Mobile Learning Adoption at a Science Museum. In: Intelligent Computing - Proceedings of the 2020 Computing Conference. Advances in Intelligent Systems and Computing . Springer-ESL, GBR, pp. 726-745. ISBN 9783030522483

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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.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2020, Springer Nature Switzerland AG.
Uncontrolled Keywords:	ict service desk,mobile learning,technological adoption,technology enhanced learning,workplace learning,control and systems engineering,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2200/2207
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	22 Nov 2023 03:47
Last Modified:	10 Dec 2024 01:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93675
DOI:	10.1007/978-3-030-52249-0_49

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