# Exploring the Determinants of Service Quality of Cloud E-Learning System for Active System Usage
## 4 Analysis and Findings
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Actual System** & **Information** & **Perceived** & **Reliability** & **Social** \\  & **Usage** & **Quality** & **Ease of Use** & **Influence** \\ \hline Actual System Usage & 0.824 & - & - & - & - \\ Information Quality & 0.692 & 0.807 & - & - & - \\ Perceived Ease of Use & 0.731 & 0.678 & 0.823 & - & - \\ Reliability & 0.637 & 0.640 & 0.493 & 0.826 & - \\ Social Influence & 0.749 & 0.726 & 0.816 & 0.548 & 0.819 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Farnell–Larcker Criterion. Table 4 states that the value of correlation with the same constructs under acceptable range (HTMT < 0.85). Finally, the outcome of the Cornell-Larcker Criterion and Heterotrait-Monotrait Ratio is evident enough for the proposed model in measuring E-Learning Service Quality. [PERSON] et al. state: \"A common threshold is a VIF value above 10 initially\". Just after a few years, [PERSON] [45] proposed a VIF value of 5 as the threshold in the context of covariance-based SEM. Finally, [PERSON] and [PERSON] [39] recommended 3.3 as the VIF threshold in the context of variance-based SEM but in discussions of formative latent variable measurement [45]. By considering all aspects this study considered the VIF threshold as 3.3, where the indicators are predictors of the latent variable score. As depicted in Table 5, VIF is less than 3 for all independent constructs and their instruments. It is evident from VIF results that no multicollinearity exists. The Structural Path Coefficients (\(\beta\) values) were calculated using SmartPLS (v. 3.3.2) and are depicted in Figures 2-4; the figures show the path coefficient diagram. T-tests with their significance level were determined and presented in Table 6 to measure the significant level of path coefficients (\(\beta\) values). As shown in Figure 2, the four constructs, namely Reliability, Information Quality, Social Influence, and Perceived Ease of Use, explained 67.4 per cent of actual device use. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Actual System** & **Information** & **Perceived** & & \\  & **Usage** & **Quality** & **Ease of Use** & **Reliability** & **Social** \\ \hline Actual System Usage & - & - & - & - & - \\ Information Quality & 0.766 & - & - & - & - \\ Perceived Ease of & 0.714 & 0.732 & - & - & - \\ Use & 0.708 & 0.731 & 0.543 & - & - \\ Reliability & 0.734 & 0.605 & 0.708 & 0.630 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Heterotrait–Monotrait Ratio. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & ASU1 & 1.405 & & \\  & ASU2 & 2.408 & & \\ Actual System Usage & ASU3 & 1.174 & 1.537 \\  & ASU4 & 1.342 & & \\  & ASU5 & 2.232 & & \\ \hline \hline \multirow{4}{*}{Information Quality} & INQ1 & 1.809 & & \\  & INQ2 & 1.483 & & \\  & INQ3 & 2.933 & 2.670 \\  & INQ4 & 2.249 & & \\  & INQ5 & 2.207 & & \\ \hline \hline \multirow{4}{*}{Perceived Ease of Use} & PEU1 & 2.660 & & \\  & PEU2 & 1.384 & & \\ \cline{1-1}  & PEU3 & 2.391 & 2.135 \\ \cline{1-1}  & PEU4 & 1.817 & & \\ \cline{1-1}  & PEU5 & 2.867 & & \\ \hline \hline \multirow{4}{*}{Reliability} & REL1 & 2.035 & & \\ \cline{1-1}  & REL2 & 2.265 & & \\ \cline{1-1}  & REL3 & 2.026 & & \\ \cline{1-1}  & REL4 & 1.699 & & \\ \hline \hline \multirow{4}{*}{Social Influence} & SOI1 & 1.982 & & \\ \cline{1-1}  & SOI2 & 2.187 & & \\ \cline{1-1}  & SOI3 & 1.963 & & \\ \cline{1-1}  & SOI4 & 2.015 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Variance Inflation Factor (VIF). Figure 4: Path Coefficient Diagram; (**a**) Information Quality to Actual System Usage; (**b**) Reliability to Actual System Usage; (**c**) Perceived Ease of Use to Actual System Usage; and (**d**) Social influence to Actual System Usage. Figure 3: Structural Path Model Coefficient. Among the four independent constructs, perceived ease of use contributes the maximum (29.1%) in actual system usage, followed by (27.4%) social influence, (26.1%) reliability and the least (12.9%) information quality in determining the actual system usage of E-Learning.