Mobile learning for just-in-time knowledge acquisition in a workplace environment

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Alade, Temitope, Welch, Ruel, Robinson, Andrew and Nichol, Lynn (2020) Mobile learning for just-in-time knowledge acquisition in a workplace environment. In: Proceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020. Proceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020 . The Institute of Electrical and Electronics Engineers (IEEE), USA, pp. 198-204. ISBN 9781728172835

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Abstract

The use of mobile devices in an educational context to support learning has drawn considerable attention, however, there is relatively little systematic knowledge about how it can be used effectively as a knowledge acquisition tool in workplace environments. This paper proposes mobile learning (m-learning) as a just-in-time learning tool to support and manage ICT problem related calls in a Science Museum (SM). Employees' intention to use m-learning is investigated using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Selected UTAUT factors including performance expectancy, effort expectancy, social influence and facilitating conditions are analysed to explain the determinants of m-learning adoption at the SM. Results demonstrate that the selected UTAUT factors had a significant impact on employee's behavioral intention to use m-learning at the SM. Further examination found age and gender moderate the relationship between the UTAUT factors. These findings present several useful implications for m-learning research and practice for ICT service desks.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2020 IEEE.
Uncontrolled Keywords:	ict service desk,mobile computing,mobile learning,technology adoption,ubiquitous learning,workplace learning,computer networks and communications,hardware and architecture,information systems and management,safety, risk, reliability and quality,control and optimization ,/dk/atira/pure/subjectarea/asjc/1700/1705
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/93677
DOI:	10.1109/ICICT50521.2020.00038

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