## 4 Results
### Hypothesis Testing  
The measurement model was evaluated for statistical significance through the process of bootstrapping [67]. \(t\)-statistics and \(p\)-values were generated for the paths between the items of each latent construct and amongst the constructs. When performing bootstrapping in smartPLS, the sub-samples used were 5000, and the one-tailed test with a 0.05 significance level was used. As for the interactions between the latent constructs, four paths that were significant are highlighted in Table 3.  
The path from motivation to behavioral intention was significant. It can be concluded that motivation is an important factor in the intention to use the CodeLab tool (Table 4). For the indirect effects, the measurement model was also bootstrapped to identify the statistical significance and as per the results the total indirect paths were not significant. The relationship between motivation (M) and the intention to use (BI) was positive and significant (Table 3), verifying H6. It can be concluded that motivation is an important construct when evaluating the intention to use e-learning systems that encourage learning-by-doing. As this was the first UTAUT evaluation of CodeLab, in the future, studies on other online learning systems that encourage learning-by-doing, not just CodeLab, can consider educational factors, such as student engagement, which is a positive outcome of student motivation. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Measurement Model** & **Structural Model** \\  & **(Preliminary Significance)** & **(Statistical Significance)** \\ \hline H1 & Performance expectancy (PE) & Reject (negative relationship) & Reject \\ H2 & Effort expectancy (EE) & Accept & Reject \\ H3 & Facilitating conditions (FCs) & Accept & Reject \\ H4 & Social influence (SI) & Reject & Reject \\ H5 & Trust in CodeLab (TC) & Reject (negative relationship) & Reject \\ H5.a & TC, EE, and SI & Accept & Accept \\ H6 & Motivation & Accept & Accept \\ H6.a & M and FCs & Accept & Accept \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hypothesis testing summary. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **EE** & **FC** & **BI** & **M** & **PE** & **SI** & **TC** \\ \hline EE & **0.179** & & & & & \\ FC & 0.788 & **0.833** & & & & \\ BI & 0.592 & 0.528 & **1.000** & & & \\ M & 0.738 & 0.641 & 0.586 & **0.901** & & \\ PE & 0.695 & 0.633 & 0.453 & 0.812 & **0.948** & \\ SI & 0.582 & 0.491 & 0.390 & 0.627 & 0.648 & **0.911** & \\ TC & 0.840 & 0.776 & 0.558 & 0.836 & 0.771 & 0.648 & **0.853** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Discriminant validity: the bolded values are AVE squares. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Path** & **\(t\)-Statistics** & \(p\)**-Values** \\ \hline EE-BI & 1.243 & 0.107 \\ FC-BI & 1.000 & 0.159 \\
**M-FC** & **6.965** & **0.000** \\
**M-BI** & **1.715** & **0.043** \\ PE-BI & 0.593 & 0.277 \\ SI-BI & 0.121 & 0.452 \\ TC-EE & 12.616 & 0.000 \\ T-BI & 0.176 & 0.430 \\ TC-SI & 8.247 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Structural model results (bold rows highlight significant paths). Additionally, there was a positive and significant relationship between motivation (M) and facilitating conditions (FCs) (Table 3), partially supporting H6.a, implying that the presence of facilitating conditions contributes to the motivation of students when using the system. Facilitating conditions, as previously mentioned, are personal qualities or factors that contribute positively to the learning process. For future evaluations of CodeLab and similar e-learning tools, in-context facilitating conditions can also be considered, for example, course forums, teachers' feedback, and channels for peer communication that facilitate the learning process. Similarly, the relationships between trust (TC) and effort expectancy (EE) and between TC and social influence (SI) were positive and significant, partially supporting H5.a, since the total indirect effect was not significant.