## 6 Practical Application and Results
### Prototyping and Evaluation
#### 6.4.2 Simulation Testing  
The SAGAT was applied following the framework outlined by [PERSON] [41; 51], as detailed below. _Experimental design._ The SAGAT experiment quantitatively measured the participants' SA while using the designed interfaces. A within-subjects design was applied, where each participant tested both the AR and MR interfaces to compare their impact on the level of SA, which is the dependent variable. The sample size was determined through a power analysis using G*Power 3.1.9.7 software. A one-tailed [PERSON]'s exact test was applied with a desired statistical power  
Figure 5: Architecture of the proposed decision support solution. of 0.80, significance level (alpha) of 0.05, and expected proportions of 0.9 and 0.4 for a large effect size, yielding a minimum of 13 participants per condition (AR/MR interface). The recruitment process was based on a minimal participant profile, requiring basic knowledge of construction practices and terminology, as the experimental task did not require advanced domain expertise. Invitations were sent via email to undergraduate and postgraduate civil engineering students listed in the department's academic database, as well as to industry practitioners within the research team's network. Ultimately, the study engaged a total of 14 volunteers, including final-year undergraduate civil engineering students and construction practitioners with experience as field managers with an average of 13.6 years of experience. Participant profiles are detailed in Table 6.  
Notably, although the number of participants in this study may seem small, it is consistent with sample sizes commonly used in SA/SAGAT research, involving realistic \begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Attribute** & **Participants (_n = 14_) (\%)** \\ \hline Occupation & Undergraduate civil engineering student & 4 (28.6\%) \\  & Construction manager & 1 (7.1\%) \\  & Field manager & 1 (7.1\%) \\  & Field inspector & 1 (7.1\%) \\  & BIM manager & 1 (7.1\%) \\  & General manager & 1 (7.1\%) \\  & Real estate manager & 1 (7.1\%) \\  & Project engineer & 1 (7.1\%) \\  & Project manager & 2 (14.3\%) \\  & R&D coordinator & 1 (7.1\%) \\ Work experience & No experience & 4 (28.6\%) \\  & 5–15 years & 7 (50.0\%) \\  & Over 16 years & 3 (21.4\%) \\ \hline \end{tabular}
\end{table} 
Table 6: Information on the study participants. Figure 6: Screenshots of the developed interfaces: (**a**) AR; and (**b**) MR.  
simulations and participants with domain-specific knowledge [43; 52; 53]. These types of studies are often exploratory in nature, with an emphasis on the depth and quality of insights obtained from each participant rather than on generalization to a broader population [54; 55]. In this context, the sample size of 14 participants was considered appropriate, as the purpose of this study was to evaluate the feasibility of the proposed interface design method and its impact on users' SA rather than to achieve generalizability. The experiment was conducted at the same building as the last test, using two Samsung Galaxy S10 Lite smartphones for the AR prototype, one Trimble XR10 and one HoloLens 2 for the MR prototype, and four laptops to administer the experiment queries (SAGAT queries). All devices were connected to the internet. This setup allowed sessions with up to four participants simultaneously. In each session, participants conducted a trial using the AR and MR prototypes, as depicted in Figure 7.  
The trial involved addressing the study goal in a simulated decision-making scenario. To minimize learning effects, two different sets of construction project information were provided (one per device) and their order was counter-balanced per participant. During the trial, participants answered the queries administered through the laptops using the Qualtrics web-based survey tool. The SAGAT queries assessed each SA requirement.