## 3 Results
### Application of the Methodology
#### 3.2.2 Analysis
These two factors represented 70,016% of our accumulated variance, with each factor being greater than 40% of the accumulated variance, with this being an adequate value for analysis. The headers are detailed at the end of the table. \begin{table}
\begin{tabular}{c c c} \hline  & **Initial** & **Extraction** \\ \hline Comfort & 0.235 & 0.344 \\ Schedules & 0.390 & 0.472 \\ Speed & 0.614 & 0.731 \\ Around & 0.606 & 0.711 \\ Privacy & 0.383 & 0.342 \\ Offer & 0.313 & 0.625 \\ \hline \end{tabular}
\end{table} 
Table 4: Communities obtained using the extraction method of maximum likelihood. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **Comfort** & **Schedules** & **Speed** & **Around** & **Privacy** & **Offer** \\ \hline \multirow{6}{*}{Anti-image covariance} & Comfort & 0.765 & \(-\)0.119 & 0.44 & \(-\)0.092 & \(-\)0.064 & 0.206 \\  & Schedules & \(-\)0.119 & 0.610 & \(-\)0.194 & \(-\)0.040 & \(-\)0.005 & 0.066 \\  & Speed & \(-\)0.044 & \(-\)0.194 & 0.386 & \(-\)0.166 & \(-\)0.057 & \(-\)0.099 \\  & Around & \(-\)0.092 & \(-\)0.040 & \(-\)0.166 & 0.394 & \(-\)0.153 & \(-\)0.125 \\  & Privacy & \(-\)0.064 & \(-\)0.005 & \(-\)0.057 & \(-\)0.153 & 0.617 & \(-\)0.097 \\  & Offer & 0.206 & 0.066 & \(-\)0.099 & \(-\)0.125 & \(-\)0.097 & 0.687 \\ \hline \multirow{6}{*}{Anti-image correlation} & Comfort & 0.731 [FOOTNOTE:1]Footnote 1: [[https://www.emf.gov/projects/co](https://www.emf.gov/projects/co)]([https://www.emf.gov/projects/co](https://www.emf.gov/projects/co))Figure 3 shows the sedimentation graph, which is a representation of the magnitude of the eigenvalues that identifies the number of values that must be extracted. The eigenvalues that explained most of the variance are located on the left side, forming a slope. The graph helped us to visually determine which were the optimal factors. Table 6 shows the matrix of rotated factors, with which each item belonging to or influencing each factor was determined. As a characteristic identified in the review of previous works, the minimum number of items for each factor must be three to four to be considered valid. In this case, the analysis generated two factors, which the average and speed items directly affected, as can be seen in the table. When there are items that affect two or more factors, these can create false incidence values or can subtract degrees of influence from other items. Therefore, it is recommended to eliminate the shared items, generate the calculation of the rotated matrix again, and verify the degrees of influence of each item on the generated factors. Figure 3: Sedimentation graph: representation of the magnitude of the eigenvalues. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Extraction Sums of Squared**} & \multicolumn{3}{c}{**Rotation Sums of Squared**} \\  & & & & & **Loadings** & & & **Loadings** \\ \hline Factor & Total & \% of variance & Cumulative \% & Total & \% of variance & Cumulative \% & Total & \% of variance & Cumulative\% \\ \hline
1 & 3.046 & 50.772 & 50.772 & 2.643 & 44.058 & 44.058 & 1.660 & 27.672 & 27.672 \\
2 & 1.155 & 19.244 & 70.016 & 0.661 & 11.021 & 55.079 & 1.644 & 27.407 & 55.079 \\
3 & 0.651 & 10.853 & 80.870 & & & & & & \\
4 & 0.507 & 8.451 & 89.320 & & & & & & \\
5 & 0.378 & 6.306 & 95.626 & & & & & & \\
6 & 0.262 & 4.374 & 100.000 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Total variance explained obtained using the extraction method of maximum likelihood. According to the results obtained, it was identified that factor one had the greatest influence on the use of eCommerce and that the perception of the clients was related to the offered item. This result was admissible, considering that the greater the offer of products and of comparisons among the products found in the digital market is, the greater the attraction of the consumer is. Factor two had an item with greater influence, which was the hours, with these being an advantage for traditional companies.