## 3 Results
### Application of the Methodology
#### 3.2.2 Analysis  
In this stage, the factorial analysis of the data obtained in the 217 surveys was carried out. Several tools allow the calculation of factor analyses to be performed to determine the level of influence that each of the variables has. Among these tools are SPSS, Excel, etc. The important thing is to know exactly what the objective of the factor analysis calculation is [39]. The analysis process was carried out in SPSS to verify, through a factor analysis, the components that affected the use of eCommerce. Table 2 shows the results obtained through a dimension reduction model. To identify the appropriate factorial model, the Kaiser-Meyer-Olkin (KMO) measure and [PERSON]'s sphericity test were used. This model tests the partial correlations among variables. In addition, [PERSON]'s test checks whether the correlation matrix is an identity matrix. KMO assumes values between 0 and 1; according to various works reviewed, for a KMO coefficient to be adequate, it must be greater than 0.6. The coefficient obtained was 0.788, so the items were taken as adequate, and the analysis continued. In [PERSON]'s test, a chi-square value is obtained that is associated with the sampling distribution that allows one to know the probability of error when rejecting a null hypothesis. The ideal case that is expected for the significance value is that this value is less than 0.005, something that was fulfilled in the analysis, with a value less than 0.001, which indicated that there were significant differences between the correlation matrix and the identity matrix. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Comfort** & **Schedules** & **Speed** & **Around** & **Privacy** & **Offer** \\ \hline
5 & 3 & 3 & 3 & 3 & 3 \\
2 & 3 & 3 & 3 & 4 & 4 \\
3 & 4 & 4 & 1 & 3 & 4 \\
4 & 4 & 4 & 5 & 3 & 3 \\ \hline \end{tabular}
\end{table} 
Table 1: Variables identified in the review of previous works with a sample of data collected through surveys. \begin{table}
\begin{tabular}{c c c} Kaiser-Meyer-Olkin Measure of Sampling & 0.788 \\ Adequacy & \\ \hline [PERSON]’s test of sphericity & Approx. chi-square & 460.453 \\ Df & 15 \\ Sig. & \textless{}0.001 \\ \end{tabular}
\end{table} 
Table 2: KMO and [PERSON]’s test. Table 3 shows the values obtained in the anti-image matrices; this type of matrix contains the negatives of the partial correlation coefficients, and the anti-image covariance matrix contains the negatives of the partial covariances. To be considered a good factor model, most of the off-diagonal elements must be small. In the results obtained in the covariance matrix, it was observed that the values were good and exceeded the 0.6 established in KMO, except for the value obtained in speed, which was a lower value, 0.386; however, in the correlation matrix, it had a value of 0.777. A similar result was obtained for the \"environment\" item. In addition, with the positive values closest to 1, a diagonal was formed, in which case, the values above the diagonal were identical to those below it, which meant that it was a mirror matrix. Table 4 shows the table of commonalities, where the percentage of variance in a variable explained by all the factors together is measured and can be interpreted as the reliability of the indicator. With the results obtained in the table, it could be highlighted that the variables of comfort and privacy had less reliability. This is because the amount of variance in all variables is explained by the eigenvalue, and if a factor has a low eigenvalue, then it contributes little to explaining the variance in the variables. Another low value was that of schedules. These three parameters were the ones that had to be analyzed in depth to determine the degree of influence that they had on the research question. Table 5 shows the matrix of the total explained variance. The results indicated that the final solution was explained by two factors or dimensions.