ITcon Vol. 30, pg. 1912-1928, http://www.itcon.org/2025/78

Beyond surveys: objective EEG-based acceptance of AR-HMDS for construction training

DOI:10.36680/j.itcon.2025.078
submitted:August 2025
revised:November 2025
published:December 2025
editor(s):Kumar B
authors:Xiaoying Yang, graduate student
Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University
ORCID: https://orcid.org/0009-0001-9900-0451
xiaoyingy@vt.edu

Hanwen Ju, graduate student (Corresponding author)
Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University
ORCID: https://orcid.org/0000-0003-1680-4698
hanwen@vt.edu

Jeremy Withers, Assistant Professor of Practice
Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University
ORCID: https://orcid.org/0009-0002-9376-3922
jeremyw7@vt.edu

Tanyel Bulbul, Associate Professor
Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University
ORCID: https://orcid.org/0000-0002-3015-5412
tanyel@vt.edu
summary:Augmented Reality Head-mounted Displays (AR-HMD) hold great promise in improving construction workers’ performance and safety through immersive training. However, their adoption in high-risk construction environments remains limited due to insufficient understanding of user acceptance. This study advances the measurement of technology adoption by integrating an extended Technology Acceptance Model (TAM) with Electroencephalogram (EEG) signals, creating a dual assessment framework that combines survey responses with objective neurophysiological indicators. The extended TAM incorporates motivational and experiential factors, while EEG captures mental workload and engagement to support the model’s constructs. Both Partial Lease Square Structural Equation Modeling (PLS-SEM) and Bayesian Structural Equation Modeling (Bayesian SEM) confirmed perceived usefulness is a central predictor of user acceptance, with enjoyment, motivational support, and perceived system quality emerging as key drivers. EEG-derived measures, validated via correlation and multiple regressions, provided converging evidence that higher motivation and stronger adoption intentions were associated with reduced cognitive workload, and that EEG ratios independently predict perceptions of usefulness, ease of use, and enjoyment. These results highlight the value of combining subjective and objective measures to inform the design of cognitively supportive, user-centered AR training systems in safety-critical construction scenarios.
keywords:AR-HMD, EEG, work at height, technology acceptance model, PLS-SEM, Bayesian SEM
full text: (PDF file, 0.889 MB)
citation:Yang X, Ju H, Withers J, Bulbul T (2025). Beyond surveys: objective EEG-based acceptance of AR-HMDS for construction training, ITcon Vol. 30, pg. 1912-1928, https://doi.org/10.36680/j.itcon.2025.078
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