# Assessing Digital Technologies' Adoption in Romanian Secondary Schools
## 4 Results
### The Main Effects  
All the hypothesized paths are statistically significant (\(p<0.001\)). Behavioral intention strongly predicts actual usage, while perceived ease of use has a greater impact on shaping behavioral intention than perceived usefulness. Teachers' perception of quality has a minor but important effect on actual usage (Table 6). The structural equation model validates the essential dynamics driving the adoption of digital educational tools and provides strong empirical support for the three proposed linkages. Hypothesis H1 posits that perceived usefulness (PU) and perceived ease of use (PEU) positively influence behavioral intention. The results strongly validate this assertion, with PEU demonstrating a more significant effect (\(\beta=0.622\), \(p<0.001\)) compared to PU (\(\beta=0.353\), \(p<0.001\)). This finding aligns with established technology acceptance literature, reinforcing that users prioritize intuitive, frictionless experiences when adopting new tools. The substantial path coefficients highlight the cognitive and practical considerations that educators evaluate before committing to digital solutions. Hypothesis H2, which asserts that behavioral intention translates into actual usage, receives unequivocal confirmation (\(\beta=0.791\), \(p<0.001\)). The high R\({}^{2}\) value (0.801) further substantiates that intention is the primary proximal determinant of real-world engagement. This strong connection suggests that once educators develop a favorable attitude toward digital tools, they are very likely to integrate them into their teaching practices. The effect size (f\({}^{2}=2.513\)) highlights the practical significance of this relationship, overshadowing other predictors in the model. Hypothesis H3 examines the dual role of behavioral intention in shaping perceptions of quality and subsequently influencing actual adoption. The analysis reveals a significant yet moderate effect of intention on perceived quality (\(\beta=0.448\), \(p<0.001\)), which in turn has a minor but meaningful impact on usage (\(\beta=0.194\), \(p<0.001\)). Although the effect sizes are more modest than those seen in direct intention-to-use pathways, they confirm that quality perceptions serve as a secondary reinforcing mechanism. Educators who recognize tangible improvements in pedagogical outcomes from digital tools demonstrate a slightly higher sustained engagement. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Coefficients** \\ **Path (c)** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **t** \\ **Statistics** \\ \end{tabular} & \
\begin{tabular}{c} \(p\) \\ **Values** \\ \end{tabular} &
\begin{tabular}{c} **Hypotheses** \\ **Validation** \\ \end{tabular} \\ \hline H1 & Perceived ease of use \(\rightarrow\) Behavioral intention & 0.791 & 0.027 & 22.988 & 0.000 & H1 validated \\ H2 & Perceived usefulness \(\rightarrow\) Behavioral intention & 0.448 & 0.030 & 11.795 & 0.000 & H2 validated \\ H2 & Behavioral intention \(\rightarrow\) Actual usage & 0.622 & 0.020 & 39.986 & 0.000 & H3 validated \\ H3 & Behavioral intention \(\rightarrow\) Teachers’ perception of quality \(\rightarrow\) Teachers’ perception of quality \(\rightarrow\) Actual usage & 0.194 & 0.048 & 9.748 & 0.000 & H3 validated \\ \hline \hline \end{tabular} Source: developed by authors using SmartPLS v3.0.  
\end{table} 
Table 6: Path coefficients.