TY - JOUR
T1 - A Technology Acceptance Model for Augmented Reality and Wearable Technologies
AU - Guest, Will
AU - Wild, Fridolin
AU - Vovk, Alla
AU - Lefrere, Paul
AU - Klemke, Roland
AU - Fominykh, Mikhail
AU - Kuula, Timo
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2018/2/28
Y1 - 2018/2/28
N2 - Leveraging Augmented Reality and wearable technology for knowledge-intensive training is thought to offer huge potential for improving human performance. The recent introduction of the technology means that much of this potential is untapped, though efforts are needed to understand what makes it useful, entertaining, and easy-to-use. The research presented in this article investigates the implementation of a combined hardware and software application in three use-cases: aviation, medical and space. Following the validation of metrics for a questionnaire, data was collected from 142 participants, and a structural equation model, based on UTAUT2, was proposed in order to interpret the data. Following model improvement, two constructs show significant factor loading and latent variable correlation, Interoperability and Augmented Reality / Wearable Technology Fit. Model optimisation was conducted, and a variety of goodness-of-fit indices are reported. The two additional constructs are found to be covariant and impact the UTAUT2 variables performance expectancy, effort expectancy and facilitating conditions, in some cases explaining more than 85% of the variance in those constructs (p < 0.001). A root mean square error of approximation of 0.047 after a 1000-fold Monte Carlo cross-validation indicates a good fit between the model and the data. In all other fit indices, a moderate power has been observed.
AB - Leveraging Augmented Reality and wearable technology for knowledge-intensive training is thought to offer huge potential for improving human performance. The recent introduction of the technology means that much of this potential is untapped, though efforts are needed to understand what makes it useful, entertaining, and easy-to-use. The research presented in this article investigates the implementation of a combined hardware and software application in three use-cases: aviation, medical and space. Following the validation of metrics for a questionnaire, data was collected from 142 participants, and a structural equation model, based on UTAUT2, was proposed in order to interpret the data. Following model improvement, two constructs show significant factor loading and latent variable correlation, Interoperability and Augmented Reality / Wearable Technology Fit. Model optimisation was conducted, and a variety of goodness-of-fit indices are reported. The two additional constructs are found to be covariant and impact the UTAUT2 variables performance expectancy, effort expectancy and facilitating conditions, in some cases explaining more than 85% of the variance in those constructs (p < 0.001). A root mean square error of approximation of 0.047 after a 1000-fold Monte Carlo cross-validation indicates a good fit between the model and the data. In all other fit indices, a moderate power has been observed.
U2 - 10.3217/jucs-024-02-0192
DO - 10.3217/jucs-024-02-0192
M3 - Article
SN - 0948-695X
VL - 24
SP - 192
EP - 219
JO - Journal of Universal Computer Science
JF - Journal of Universal Computer Science
IS - 2
ER -