Using the Unified Theory of Acceptance and Use of Technology (UTAUT) Model to Determine Factors Affecting Mobile Learning Adoption in The Workplace: A Study of The Science Museum Group

Welch, Ruel, Alade, Temitope and Nichol, Lynn (2020) Using the Unified Theory of Acceptance and Use of Technology (UTAUT) Model to Determine Factors Affecting Mobile Learning Adoption in The Workplace: A Study of The Science Museum Group. In: IADIS International Journal on Computer Science and Information Systems. UNSPECIFIED.

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Abstract

It has been observed that mobile learning (mLearning) in institutions like Museums in the United Kingdom (UK) has been underutilized. mLearning usage could potentially increase productivity by delivering just-in-time technical knowledge to the science museum group (SMG) staff. This study uses the unified theory of acceptance and use of technology (UTAUT) model to determine factors affecting mLearning adoption at the SMG. Two research questions were formulated based on an adaptation of the UTAUT model. 1) What are the determinants of behavior intentions to use mLearning at the SMG? 2) Does gender or age have a moderating effect on the factors that determine behavior intentions to use mLearning at the SMG? 118 respondents were surveyed from the SMG. Data obtained were analyzed using Structured Equation Modelling on IBM SPSS 20 and Amos version 25. Results indicate that the UTAUT constructs, performance expectancy, effort expectancy, social influence and facilitating conditions are all significant determinants of behavioral 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 SMG. The research contributes to mLearning technology adoption and strategy.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://www.iadisportal.org/ijcsis/paper...
Depositing User:	LivePure Connector
Date Deposited:	22 Nov 2023 03:48
Last Modified:	10 Dec 2024 01:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93695
DOI:

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