Cited by 26Technology Adoption and User BehaviourAugmented Reality ApplicationsVirtual Reality Applications and Impacts

Evaluating Students’ Acceptance Intention of Augmented Reality in Automation Systems Using the Technology Acceptance Model

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Shyr, Wen-Jye, Wei, Bo-Lin, Liang, Yu-Chuan·2024·Sustainability

Abstract

This study aims to evaluate the factors influencing the acceptance of augmented reality among students within an automation system. The researchers of this study adopted the technology acceptance model (TAM) and gathered data to examine the path relationships among key variables: perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention to use, all of which impact system acceptance. A survey was administered to students to gauge their perspectives on integrating augmented reality into the automation system. The study sample consisted of 41 students enrolled in the automation system course where augmented reality was utilized in teaching. Utilizing the partial least square approach, the hypothesis model was analyzed. The results revealed a lack of significant correlation between perceived ease of use and perceived usefulness in relation to augmented reality usage. However, perceived ease of use positively influences students’ behavioral intention to use, while perceived usefulness significantly affects behavioral intention to use through augmented reality. Notably, perceived usefulness exhibits a significant relationship with both behavioral intention to use and attitude toward use in the context of augmented reality usage. Moreover, perceived ease of use positively affects attitude toward use for students within the context of automation systems with augmented reality. Furthermore, no significant relationships were observed between attitude toward use and behavioral intention to use, nor between perceived ease of use and perceived usefulness regarding augmented reality usage. This study offers valuable insights for educators in the field of engineering and technology who are engaged in training and sustainability development programs.

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  1. Abdallah, I.; Dertimanis, V.; Mylonas, H.; Tatsis, K.; Chatzi, E.; Dervilis, N.; Worden, K.; Maguire, E. Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data. In Safety and Reliability-Safe Societies in a Changing World; CRC Press: Boca Raton, FL, USA, 2018; pp. 3053-3061.
  2. Wan, Z.; Wan, Y.; Tengku, B.S.; Tengku, Y.; Noniwati, A. Evaluating student's acceptance of google classroom in co-curricular photography course using technology acceptance model (TAM). Int. J. Technol. Vocat. Educ. Train. 2023, 4, 28-34.
  3. Cabero-Almenara, J.; Barroso-Osuna, J.; Llorente Cejudo, M.d.C. Technology Acceptance Model & augmented reality: Study in progress. Rev. Lasallista Investig. 2016, 13, 18-26.
  4. Asad, M.M.; Hassan, R.B.; Sherwani, E.F. Instructional models for enhancing the performance of students and workforce during educational training. Acad. Arena 2014, 6, 27-31.
  5. Hair, F.; Black, C.; Babin, J.; Anderson, E. Multivariate Data Analysis, 7th ed.; Pearson Education Limited: London, UK, 2014.
  6. Lai, C.M.; Huang, H.M.; Liaw, S.S.; Huang, W.W. A study of user's acceptance on three dimensional virtual reality applied in medical education. Bull. Educ. Psychol. 2009, 40, 341-362.

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Authors
Shyr, W.-J., Wei, B.-L., & Liang, Y.-C.
Affiliations
National Changhua University of Education (TW)
Corresponding author
Shyr, Wen-Jye
Venue
Sustainability
Publisher
MDPI AG
Published
2024-02-29
Volume
16
Issue
5
Pages
2015
ISSN
2071-1050 (ISSN-L)
OpenAlex
W4392294880
Cited by
26
References
58
Citations by year
2026: 8 · 2025: 14 · 2024: 4
FWCI
9.74
Type
journal article
Language
en
Open access
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
  • Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
Keywords
augmented reality, technology acceptance, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention, engineering students, educational technology adoption

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