## 2 Materials and Methods
### Data Collection, Exploratory Factor Analysis, and the Parameters of the Algorithm Run  
The numerical scale of the survey, as well as aspects of the respondent public and the sample size, are discussed in this subsection. Figure 3: The reflective structure with indicators, constructs, and hypotheses is depicted in SmartPLS 4. Figure 2: The structure is divided into both a structural model and a reflective measurement model. The adoption of a numerical scale for the answer options was needed to facilitate measurement with greater accuracy of non-metric answers. Employing a 5-point scale offered benefits, like a higher explained variance (42.9%) than the 3-point (31.1%) and the 7-point (41.5%) scales in an empirical study [43]. Also, in another study, the 5-point response option produced fairly symmetric and unimodal distributions, differently from the highly skewed J- and U-shaped ones from the 10-point version [44]. In addition, [PERSON] et al. [45] affirm in a review article that among 60 articles examined, the 5-point scale is the most common. Thus, 5 points have been adopted in the present study. For the \"10-times rule\", the smallest number of respondents should be equal to ten times the largest number of structural paths directed at a construct [46], thus \(10\times 4=40\). As to the questionnaire distribution, several attempts were made to obtain responses, for example, publication on LinkedIn, private distribution via LinkedIn, and e-mail. They were sequentially tried to obtain a number of respondents that not only met the 10-times rule but also better represented the Salvador metropolis' order of magnitude. All these tries were very frustrated until it was decided to send the questionnaire privately via a messenger application. A total of 71 of Salvador's inhabitants among both the academic and practitioner public agreed to answer. The electronically delivered questionnaire (Table A1) collected the respondent's age, level of education, and field of study in Part 1. A summary of this part's answers is shown in Table 5.  
In succession, part 2 of the questionnaire collected their opinions about the degree of each indicator's positive influence on the performance of collection, sorting, and recycling in Salvador. Electricity generation through waste-to-energy was considered when recycling was not possible. The available answer options ranged from 1 to 5. The data collected from the respondents are shown in Appendix C, Table A2, where each column is a question and each row is a respondent. Only the answers to the 14 questions about plastic waste management, all of them from part 2, were entered into the model as raw data. As this study is exploratory because it constructs a new model starting from separated indicators and then assesses the contribution of each of them, the \(p\)-value criterion of 0.10 was adopted throughout this paper, following the considerations of [PERSON] et al. [23] for studies of this kind. The Kaiser-Meyer-Olkin (KMO) test was run using the KMO command from the psych library in the RStudio software, 2023.03.0-386 version, to measure the dataset ad  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Age (Years)** & **Frequency** & **Relative Frequency** & **Field of Study** & **Frequency** & **Relative Frequency** \\ \hline \(<\)24 & 16 & 22.54\% & Industrial Engineering & 16 & 22.54\% \\
25-34 & 39 & 54.93\% & Agricultural Engineering & 9 & 12.68\% \\
35-44 & 8 & 11.27\% & Administration & 7 & 9.86\% \\
45-54 & 2 & 2.82\% & Mechanical Engineering & 5 & 7.04\% \\
55-64 & 3 & 4.23\% & Electrical Engineering & 5 & 7.04\% \\ Unidentified & 3 & 4.23\% & Civil Engineering & 3 & 4.23\% \\  & & & Control and Automation Engineering & 2 & 2.82\% \\ \hline \hline \multirow{3}{*}{**Level of Education**} & \multirow{3}{*}{**Frequency**} & **Relative Frequency** & Sanitary and Environmental & 2 & 2.82\% \\  & & & **Frequency** & Enginerning & 2 & 2.82\% \\ \cline{1-1} \cline{3-5} \cline{  
equacy for sampling [47; 48; 49].