# Assessing Digital Technologies' Adoption in Romanian Secondary Schools
## 4 Results
### Validity and Reliability  
The model demonstrates strong predictive validity, as evidenced by the R\({}^{2}\) values. Actual usage (R\({}^{2}\) = 0.801) and behavioral intention (R\({}^{2}\) = 0.800) exhibit a high explanatory power, indicating that the predictors account for a substantial portion of the variance in these constructs. However, teachers' perception of quality shows a weaker yet significant explained variance (R\({}^{2}\) = 0.200). The effect sizes (f\({}^{2}\)) reveal the relative impact of each predictor. Behavioral intention exerts a powerful influence on actual usage (f\({}^{2}\) = 2.513), while perceived ease of use significantly affects behavioral intention (f\({}^{2}\) = 1.096). Perceived usefulness has a moderate effect (f\({}^{2}\) = 0.354), and teachers' perception of quality shows a more minor yet meaningful contribution (f\({}^{2}\) = 0.151) (Table 2).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Original** & **Standard** & **t** & **\(p\)** \\  & **Sample** & **Deviation** & **Statistics** & **Values** \\ \hline Actual usage & 0.801 & 0.014 & 55.865 & 0.000 \\ Behavioral intention & 0.800 & 0.017 & 47.751 & 0.000 \\ Teachers’ perception of quality & 0.200 & 0.040 & 5.053 & 0.000 \\ \hline \hline \end{tabular} Source: developed by authors using SmartPLS v3.0.  
\end{table}
Table 2: R squared.  
Figure 2: Empirical model. Source: developed by authors using SmartPLS v3.0.  
These findings emphasize the significant role of behavioral intention in influencing actual usage, reinforcing the TAM framework. The considerable impact of perceived ease of use highlights its importance in shaping user attitudes.  
The variance inflation factor (VIF) values for all indicators remain below the critical threshold of 5, with the highest being flexibility (VIF = 2.437) (Table 3).  
This result suggests that multicollinearity does not distort the regression estimates. Internal consistency and convergent validity were assessed using [PERSON]'s alpha, composite reliability, and average variance extracted (AVE). All constructs exceeded the recommended thresholds ([PERSON]'s \(\alpha\) > 0.7, AVE > 0.5), affirming reliability (Table 4).  
The high AVE values (above 0.66) support convergent validity, ensuring that each construct captures a distinct variance.  
Since the square root of each construct's AVE is greater than its correlations with other components, the Cornell-Larcker criteria support discriminant validity. For example, perceived usefulness and ease of use exhibit a lower within-construct correlation (Table 5).  
This analysis confirms that the constructs are empirically distinct, further supporting the model's validity. The standardized root mean square residual (SRMR = 0.072) is below the 0.08 cutoff, indicating a good fit. The normed fit index (NFI = 0.914) also suggests a good fit.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Cronbach’s** & **Composite** & **Average Variance** \\  & **Alpha** & **Reliability** & **Extracted (AVE)** \\ \hline Behavioral intention & 0.803 & 0.910 & 0.835 \\ Perceived ease of use & 0.876 & 0.914 & 0.728 \\ Perceived usefulness & 0.829 & 0.886 & 0.661 \\ Teachers’ perception of quality & 1.000 & 1.000 & 1.000 \\ Actual usage & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular} Source: developed by authors using SmartPLS v3.0.  
\end{table}
Table 4: Reliability and validity assessment.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Variable** & **VIF** \\ \hline Attitude\_towards\_use & 1.821 \\ Convenience & 1.936 \\ Cost & 1.758 \\ Customization & 2.247 \\ Enjoyment & 1.954 \\ Extent\_of\_use & 1.000 \\ Flexibility & 2.437 \\ Informativeness & 1.568 \\ Innovation & 2.053 \\ Intention\_to\_use & 1.821 \\ Quality & 1.000 \\ Speed & 2.262 \\ \hline \hline \end{tabular} Source: developed by authors using SmartPLS v3.0.  
\end{table}
Table 3: Variance inflation factor.