## 3 Results
### Application of the Methodology
#### 3.2.2 Analysis
Another item identified was the privacy of factor one, at present, due to the large volume of computer attacks that seek to obtain, modify, or delete user data. This item had a great impact on the use of eCommerce by people; therefore, it is important to establish models and architectures in the migration to a digital environment that comply with all information security and privacy measures. To validate the analysis, it is recommended to use Cronbach's alpha coefficient. Cronbach's alpha is the reliability indicator of psychometric scales that provides a measure of the internal consistency of the items that make up a scale. If this measure is high, it implies evidence of the homogeneity of the scale, that is, that the elements point in the same direction. For example, in Table 7, the calculation results of Cronbach's alpha are shown, and the data considered were obtained from the above-mentioned 217 records; however, for the example in the table, only the first 20 are presented. In the table, the dotted spaces mean that there were multiple records in the original repository. The last row contains the results of the calculation of the variance based on the entire population. The total column is the sum of all the values of each of the categories that were included in the analysis. In the calculation of Cronbach's alpha, the variances of each category were used plus the total variance shown in the last column. These data were applied using the formula presented below:  
\[\propto=\left[\frac{k}{k-1}\right]\left[1-\frac{\sum_{i=1}^{k}S_{i}^{2}}{S_{t }^{2}}\right] \tag{2}\]  
where the following apply:  
* \(S_{i}\) is the variance of item \(i\);
* \(S_{t}\) the variance of the total observed values;
* \(k\) is the number of questions or items;  
The results of [PERSON]'s alpha are presented in Table 8. This consisted of three calculations or interactions, and interaction 1 presents the result of the calculation of the six items. According to the values obtained in the variances, three values were identified that were significant for the analyses. These values were comfort, privacy, and supply; the variances in these items indicated that their incidence could affect the coefficient of [PERSON]'s alpha. Therefore, in interaction 2, the supply data were eliminated, and the result increased to be greater than 0.8, which indicated that the analysis was more representative, as it did not consider this item. The process was carried out with each item that presented variance values close to zero; however, by removing the privacy value, the alpha coefficient reduced, which meant that it was a parameter that had a great influence on the analyses. The opposite happened when we removed the comfort item; without this value, the coefficient was closer to one and exceeded 0.8, which implied that the analysis was better and had greater reliability. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **1** & **2** \\ \hline Offer & 0.788 & \\ Around & 0.630 & 0.560 \\ Privacy & 0.515 & \\ Schedules & & 0.648 \\ Speed & 0.568 & 0.639 \\ Comfort & & 0.585 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Rotated factor matrix obtained using the extraction method of maximum likelihood and the rotation method of Varimax with Kaiser normalization, with rotation converged in 3 iterations. This process can answer several research questions one may have about small business migration to eCommerce. However, it is important to point out that this first analysis integrated the variables identified in previous works. If the answer is not reliable enough, it is necessary to integrate a greater number of variables or data sources. Additional data sources can be of various nature, as mentioned in the above sections. The most common example of external sources are surveys that are found on the web and are considered open data. Other possible sources to be added are social networks; these contain a high volume of information that can be analyzed about the perception of users relative to a brand, product, or service.