A technology acceptance model for augmented reality and wearable technologies

William Guest, Fridolin Wild, Alla Vovk, Paul Lefrere, Roland Klemke, Mikhail Fominykh, Timo Kuula

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)192-219
Number of pages28
JournalJournal of Universal Computer Science
Volume24
Issue number2
Publication statusPublished - 1 Jan 2018
MoE publication typeA1 Journal article-refereed

Fingerprint

Technology Acceptance
Augmented reality
Augmented Reality
Human Performance
Structural Equation Model
Aviation
Latent Variables
Goodness of fit
Use Case
Optimization Model
Cross-validation
Mean square error
Interoperability
Questionnaire
Fold
Application programs
Roots
Hardware
Model
Model-based

Keywords

  • Augmented reality
  • Microsoft hololens
  • Structural equation model
  • Technology acceptance
  • UTAUT2
  • Wearable technology

Cite this

Guest, W., Wild, F., Vovk, A., Lefrere, P., Klemke, R., Fominykh, M., & Kuula, T. (2018). A technology acceptance model for augmented reality and wearable technologies. Journal of Universal Computer Science, 24(2), 192-219.
Guest, William ; Wild, Fridolin ; Vovk, Alla ; Lefrere, Paul ; Klemke, Roland ; Fominykh, Mikhail ; Kuula, Timo. / A technology acceptance model for augmented reality and wearable technologies. In: Journal of Universal Computer Science. 2018 ; Vol. 24, No. 2. pp. 192-219.
@article{d28765ad18424b559ef0c72e28f5de65,
title = "A technology acceptance model for augmented reality and wearable technologies",
abstract = "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.",
keywords = "Augmented reality, Microsoft hololens, Structural equation model, Technology acceptance, UTAUT2, Wearable technology",
author = "William Guest and Fridolin Wild and Alla Vovk and Paul Lefrere and Roland Klemke and Mikhail Fominykh and Timo Kuula",
year = "2018",
month = "1",
day = "1",
language = "English",
volume = "24",
pages = "192--219",
journal = "Journal of Universal Computer Science",
issn = "0948-695X",
publisher = "Technische Universitat Graz from Austria",
number = "2",

}

Guest, W, Wild, F, Vovk, A, Lefrere, P, Klemke, R, Fominykh, M & Kuula, T 2018, 'A technology acceptance model for augmented reality and wearable technologies', Journal of Universal Computer Science, vol. 24, no. 2, pp. 192-219.

A technology acceptance model for augmented reality and wearable technologies. / Guest, William; Wild, Fridolin; Vovk, Alla; Lefrere, Paul; Klemke, Roland; Fominykh, Mikhail; Kuula, Timo.

In: Journal of Universal Computer Science, Vol. 24, No. 2, 01.01.2018, p. 192-219.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - A technology acceptance model for augmented reality and wearable technologies

AU - Guest, William

AU - Wild, Fridolin

AU - Vovk, Alla

AU - Lefrere, Paul

AU - Klemke, Roland

AU - Fominykh, Mikhail

AU - Kuula, Timo

PY - 2018/1/1

Y1 - 2018/1/1

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.

KW - Augmented reality

KW - Microsoft hololens

KW - Structural equation model

KW - Technology acceptance

KW - UTAUT2

KW - Wearable technology

UR - http://www.scopus.com/inward/record.url?scp=85048874994&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85048874994

VL - 24

SP - 192

EP - 219

JO - Journal of Universal Computer Science

JF - Journal of Universal Computer Science

SN - 0948-695X

IS - 2

ER -

Guest W, Wild F, Vovk A, Lefrere P, Klemke R, Fominykh M et al. A technology acceptance model for augmented reality and wearable technologies. Journal of Universal Computer Science. 2018 Jan 1;24(2):192-219.