## 4 Research Execution and Analysis
### EFA Indicator Classification  
To obtain data more conveniently and accurately, we utilized SPSS 27.0 to compute the questionnaire metrics of KMO, [PERSON]'s test of sphericity, and [PERSON]'s alpha coefficient. The outcomes, as delineated in Table 9, are a KMO measure of 0.923, a [PERSON]'s test significance below 0.05, and a [PERSON]'s alpha of 0.923. These findings collectively affirm the high reliability and validity of the questionnaire data, which thereby qualify for subsequent EFA investigation. As part of this factor analysis, Exploratory Factor Analysis (EFA) was employed to distill the factor structure from the data. During the factor extraction process, only factors with eigenvalues \(\lambda\) greater than 1 were retained, eliminating any factors constituted by a single item. When the loading difference between two items on the same factor was less than 0.05, one of the items was removed, and the analysis was then re-conducted. Additionally, any item with a communality less than 0.4 or a maximum loading less than 0.35 was also excluded. After several rounds of screening and rotation, five common factors with eigenvalues \(\lambda\) greater than 1 were ultimately identified, retaining 23 metrics related to the usability of second-hand trading software. Utilizing the described analytical techniques, we ascertained the contribution of each factor to the total variance, as meticulously outlined in Table 10. Further insights are provided by the scree plot in Figure 2, which elaborates on each factor's contribution toward the total variance. Moreover, the correlations between various usability metrics of second-hand trading software (such as user satisfaction, efficiency in operation, and ease of recall) and the identified factors (e.g., B1, B2, etc.) are detailed in Table 11.  
Figure 2: Scree plot. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Second-Hand Trading App Usability Indicators**} \\ \hline Learnability & Efficiency & Memorability & Error Prevention & Satisfaction & Flexibility \\ \hline Simplicity in Use & Immediate Feedback & Consistency & Help & Privacy Protection & Property Protection \\ \hline Interface Simplicity & Findability & Accessibility & Value Realization & Convenience & Pleasure \\ \hline Social & System Security & Page Information Architecture & Navigation & Error Feedback & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Indicators after Delphi questionnaire. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Total Variance Explained**} \\ \hline  & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Sum of Squared Loadings for Extraction**} & \multicolumn{3}{c}{**Rotated Sum of Squared Loadings**} \\ \cline{2-10} \multirow{3}{*}{**Element**} & \multirow{3}{*}{**Total**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Cumulative**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Cumulative**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Cumulative**} & \multicolumn{2}{c}{**Percentage**} \\  & & **of Variance** & **Percentage** & **of Variance** & **Percentage** & **of Variance** & **Percentage** & **of Variance** & **Percentage** & **of Variance** & **of Variance** \\  & & **explained** & **explained** & **explained** & **Explained** & **Explained** & **Explained** & **Explained** \\ \hline
1 & 8.616 & 37.463 & 37.463 & 8.616 & 37.463 & 37.463 & 4.034 & 17.538 & 17.538 \\ \hline
2 & 2.269 & 9.866 & 47.32  
Based on the results from the rotated component matrix in Table 11 and the characteristics of indicators for each dimension, we extracted and named the following common factors. The first group (B1) of factors includes indicators such as Satisfaction, Flexibility, Value Realization, Pleasure, Accessibility, and Social Interaction.