## 3 Results of the PLS-SEM Path Model Estimation
### Assessing the PLS-SEM Results of the Structural Model
Path coefficients below 0.10 are not considered statistically significant, while those exceeding 0.20 are typically significant. It is important to note that, at a 10% significance level, the t-value should exceed 1.65 for a two-tailed test, as stipulated by [23]. For assessing the effect of excluding an exogenous construct from the model, the use of the effect size (\(\ell^{2}\)) is necessary, as proposed by [54]. The measurement of each construct can result in small, medium, or large effect sizes, as respectively indicated by values around 0.02, 0.15, or 0.35 [23,46]. Table 8 shows all the values, being the sample mean computed considering all the 5000 bootstrapping samples. According to Table 8, all the exogenous constructs on the left showed a positive influence on performance, except efficiency. The negative sign in the path coefficient for EFICI is an undesired consequence of a mistake. This is explained because its remaining indicator, EFICI-3-variability (impurity), has meaning in itself not beneficial for the performance in the respondents' opinions as if it were a matter of inefficiency instead of efficiency. The reader, if they want, is free to think that if inefficiency obtained a coefficient of \(-0.072\), then the correspondence for efficiency is 0.072. Thus, the purity of the waste must favor performance [1]. Similarly, the hypothesis would not be supported, and the indicator's inherent meaning should be corrected for broader versions of this research in the future. The hypotheses H2 and H4--respectively, the direct correlation of the EFICA construct on performance, and the direct correlation of the INFRA construct on performance--were supported. H3, related to the municipality's socioeconomic aspects, could be supported because of its sample mean \(\beta\) of 0.100, but the original \(\beta\) is 0.097 and the t-value is below the threshold. Socioeconomic issues divide opinions and may need a better specification of indicators for conclusive findings. The Q\({}^{2}\), also known as predictive relevance or out-of-sample predictive power, measures how well the path model can predict the original values in the dataset. The effect size (q\({}^{2}\)) compares the relative impact of the constructs' predictive relevance [23]. The Q\({}^{2}\) and q\({}^{2}\) could not be measured in SmartPLS 4 because they are not available in the free version that the authors were able to access until the conclusion of this exploratory study. Instead, the R language in RStudio was used. R's semirr library also allows for creating and estimating structural equation models through the command predict_pls. It does not directly calculate the Q\({}^{2}\) and q\({}^{2}\) values, but \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Hypothesis** & **VIF** & **Original R\({}^{2}\)** & \begin{tabular}{c} **Sample** \\ **Mean 1 R\({}^{2}\)** \\ \end{tabular} & **Original \(\beta\)** & \
\begin{tabular}{c} **Sample** \\ **Mean 1 \(\beta\)** \\ \end{tabular} & **Original \(\ell^{2}\)** &
\begin{tabular}{c} **Sample** \\ **Mean 1 \(\ell^{2}\)** \\ \end{tabular} & **Standard** & **Error\({}^{1}\)** & \(t\)-Value 2** & **Decision** \\ \hline H1: EFICAI\({}^{\bot}\) & 1.181 & 0.573 & 0.606 & \(-0.069\) & \(-0.072\) & 0.010 & \(-0.072\) & 0.097 & 0.717 & Not Supported \\ \hline H2: EFICA\({}^{\bot}\) & 1.576 & 0.573 & 0.606 & 0.493 & 0.492 & 0.361 & 0.492 & 0.105 & 4.671 & Supported \\ \hline HS: SCOO \({}^{\bot}\) & 1.431 & 0.573 & 0.606 & 0.097 & 0.100 & 0.015 & 0.100 & 0.095 & 1.026 & Not Supported \\ \hline H4: INFRA\({}^{\bot}\) & 1.485 & 0.573 & 0.606 & 0.283 & 0.286 & 0.126 & 0.286 & 0.110 & 2.574 & Supported \\ \hline \hline \end{tabular}  
* Bias-corrected and accelerated bootstrapping. 2 Statistical significance is confirmed when \(t\)-value \(>\) 1.65 (\(p\)-value \(<\) 0.10).  
\end{table} 
Table 8: Decision about the hypotheses considering the VIFs, coefficients of determination (R\({}^{2}\)), path coefficients (\(\beta\)), effect sizes (\(\ell^{2}\)), standard errors, and t-values.