# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 4 Results
### Structural Model of the Relationship Between Factors and the Perceived Practicality of VR  
Following the prior adjustments and refinements, the current analysis explores the relationship between several independent variables and the latent variable perceived prac  
Figure 2: Unifactorial Measurement Model for the latent factor perceived practicality of VR across project lifecycle stages with estimated factor loadings and covariances (**\(p<0.01\), ***\(p<0.001\)). ticality of VR. These independent variables include age (categorized as 20-30, 31-40, 41-50, 51-60, and above 60), education level (Technician/Practical Engineer, Bachelor's degree, and Master's degree or higher), experience in engineering (less than a year, 1-5 years, 6-10 years, 11-20 years, and above 20 years), and tenure in the current organization (less than a year, 1-5 years, 6-10 years, 11-20 years, and above 20 years). Additionally, domain size is considered, defined by the number of employees in the organizational unit (1-10, 11-20, 51-100, 51-100, and more than 100). The analysis also includes perceptual and organizational variables such as the extent to which the organization encourages the use of innovative technologies, the respondent's self-rated familiarity with VR, the degree to which their work is connected to VR, and their likelihood of adopting VR solutions in systems engineering processes. All variables are measured on ordinal scales and are incorporated to capture both demographic and perceptual dimensions relevant to the perceived practicality of VR. The diagram in Figure 3 presents the structural model, showing how each independent variable contributes to explaining the latent variable of perceived practicality of VR. The model was estimated using the DWLS method with optimization via NLMINB. The model fit was acceptable: \(\chi^{2}(63)\) = 71.87, \(p\) = 0.21; CFI = 0.95; TLI = 0.98; RMSEA = 0.05 (90% CI [0.00, 0.10]); SRMR = 0.07. These results indicate a good fit of the structural model to the data. In the structural equation model, three predictors were found to have statistically significant path coefficients in predicting the latent variable perceived practicality of VR. Education demonstrated a significant negative relationship (b = \(-\)0.36, SE = 0.17, z = \(-\)2.17, \(p\) = 0.03; \(\beta\) = \(-\)0.34), indicating that **a higher level of education is associated with lower perceived practicality of VR**. Familiarity with VR also showed a significant negative association (b = \(-\)0.32, SE = 0.13, z = \(-\)2.54, \(p\) = 0.01; \(\beta\) = \(-\)0.43), suggesting that greater self-reported familiarity corresponds to lower perceived practicality. In contrast, the likelihood of adopting VR in systems engineering processes was positively associated with the perceived practicality of VR (b = 0.21, SE = 0.09, z = 2.40, \(p\) = 0.02; \(\beta\) = 0.39), indicating that respondents who were more willing to adopt VR reported  
Figure 3: Structural model of the relationship between factors and the Perceived Practicality of VR (\({}^{*}p<0.05\), \({}^{**}p<0.01\), \({}^{***}p<0.001\)). higher levels of perceived practicality of VR. Other structural paths--including those from age, engineering experience, tenure, domain size, organizational encouragement of innovation, and work connection to VR--were not statistically significant (all \(p>0.05\)). These findings suggest that perceptual and motivational factors are more influential than demographic or organizational ones in shaping how individuals evaluate the practical applicability of VR in systems engineering. In the next stage, the relationship between the nine independent variables (outlined in Section 4.3) and the perceived practicality of VR at each project lifecycle stage was examined. Both the independent variables and the outcome measures were treated as ordered categorical variables. The model was estimated using the DWLS estimator with optimization via the NLMINB method.