## 3 Results
### Application of the Methodology
#### 3.2.2 Analysis
In this work, several additional questions were added to evaluate new categories, and new surveys were designed to obtain data. The new categories added sought to identify all the possible causes that were marked as the main points for people to migrate from traditional markets to eCommerce. Many categories can be included in this process; however, each of the new categories must be analyzed by each department in charge of market research. In this work, six additional categories were integrated. In each category, two items or questions were included to identify the perception of users. The questions for each category are presented below:  
* Accessibility: 1. Is it easy to find what I need on the website? 2. Does the website allow me to complete a transaction quickly? * Catalogs: 1. Did you find enough product details? 2. Do you usually find several alternatives for the same product? \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Comfort** & **Schedules** & **Speed** & **Around** & **Privacy** & **Offer** & **Total** \\ \hline  
Var.P & 1.854870564 & 2.169678694 & 2.159570176 & 2.137484338 & 1.630656841 & 1.377858948 & 33.66013294 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Calculation of [PERSON]’s alpha for the factors identified in the factorial analysis. * Compliance: a Do you deliver the order when promised? b. Does the website have reliable offers? * Support: a. Do you have an online customer service? b. How helpful was the customer service staff? * Satisfaction (consumer experience): a. Was my decision to buy on this website a wise one? b. Does the performance of this website meet my expectations? * Shipment: a. Did you receive your product at the shipping address? b. Has the order been delivered on time? From the data analysis of the categories attached to the process, several results were obtained that guided the understanding of what consumers looked for in an eCommerce model. Table 9 shows the results; the KMO coefficient was 0.763, a value that allowed the analysis to be defined as valid, considering in addition that the significance value was less than 0.005, reaffirming its validity. In Table 10, the anti-image values of covariance and correlation are presented. In this analysis, the incidence of each of the items and how this determines the different factors or dimensions considered for the evaluation of the data are observed. In the correlation of the diagonal values, it was obtained that they exceeded 0.3; however, there were values of certain items that could be considered as the minimum and could be eliminated to improve the response of the analysis. In this table, item 1 had a correlation value of 0.450; this was a low value that was kept in mind to compare it with the following tables and eliminate it if its influence on the object of study was not relevant. In the table, the headings correspond to the questions in each category; these were replaced by the letter \"P\", accompanied by a number as an identifier, for example, Q1: Is it easy to find what I need on the website? This format was followed in all tables so that they fit the size of the format. Table 11 presents the results of the commonalities, where it was identified that items 1 and 2 had low extraction values of 0.012 and 0.044 respectively. As valid values in the analysis, it was established that all those items with an extraction value greater than 0.400 were valid in commonality processing. The analysis defined four factors to reach the percentage of the accumulated variance of 65.492%; this was a valid value to consider in the analysis. Table 12 shows the factors and the items that were directly aligned with each of them. The factors to be accepted within the analysis had to integrate at least three items. This was true in the matrix of rotated factors; however, each factor had to be reviewed, since there were values that did not influence the decision of consumers.