## 3 Methodology
### Research Methods and Survey Techniques
#### 3.2.2 Exploratory Factor Analysis  
Exploratory Factor Analysis (EFA) was initially proposed by [PERSON] in 1904 as a multivariate statistical technique aimed at reducing data into a smaller set of summary variables and exploring the underlying theoretical structure of phenomena [44; 45].  
Questionnaire Design  
Based on the usability indicators obtained through the Delphi survey method, an online questionnaire was created on the Questionnaire Star platform (Wenjuanxing: [[http://www.wjx.cn](http://www.wjx.cn)]([http://www.wjx.cn](http://www.wjx.cn)) 16 March 2024) to collect the data required for Exploratory Factor Analysis (EFA). The Questionnaire Star platform has been widely used in academic research, as demonstrated by studies conducted by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and others, all of whom utilized the Questionnaire Star platform for their online surveys [40; 41; 42]. The online questionnaire provides a convenient option for reaching a large audience. Reverse questions were included in the survey to reduce response bias and enhance the reliability and validity of the questionnaire [46].  
Responses to the reverse questionnaire that were incorrect were considered invalid. The questionnaire was conducted from 14 March 2024 to 17 March 2024. Based on a review of the literature on survey sample sizes [47; 48; 49], a total of 412 users aged 18 and above who had actually used second-hand trading software were surveyed. The participant information is detailed in Table 2. A total of 412 questionnaires were distributed, of which 400 were valid and 12 were invalid. Among the invalid questionnaires, 8 had identical answers throughout, and 4 had incorrect responses to the reverse questions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(N=20\) & **Item** &
\begin{tabular}{c} **Number of** \\ **Participants** \\ \end{tabular} & **Percentage (\%)** \\ \hline \multirow{3}{*}{Age Range (years)} & 30–34 & 7 & 35 \\ \cline{2-4}  & 35–39 & 9 & 45 \\ \cline{2-4}  & 40–44 & 3 & 15 \\ \cline{2-4}  & Over 45 & 1 & 5 \\ \hline \multirow{3}{*}{Gender} & M & 8 & 40 \\ \cline{2-4}  & F & 16 & 60 \\ \hline \multirow{3}{*}{Occupation} & Designer & 10 & 50 \\ \cline{2-4}  & Design Professor & 5 & 25 \\ \cline{2-4}  & Environmental Science Professor & 2 & 10 \\ \cline{2-4}  & Software Engineer & 3 & 15 \\ \hline \multirow{3}{*}{Education Level} & Bachelor’s Degree & 7 & 35 \\ \cline{2-4}  & Master’s Degree & 8 & 40 \\ \cline{2-4}  & Ph.D. & 5 & 25 \\ \hline \multirow{3}{*}{Work Experience (years)} & 5–9 & 5 & 25 \\ \cline{2-4}  & 10–14 & 14 & 70 \\ \cline{1-1} \cline{2-4}  & More than 15 & 1 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic information of the expert Delphi panel.  
Research Process  
Before performing an Exploratory Factor Analysis (EFA) of the data collected from online questionnaires, it is crucial to evaluate the validity and reliability of the data, determining their fitness for EFA. When the Kaiser-Meyer-Olkin (KMO) value is higher than 0.7, and the significance level in the Bartlett's Test of Sphericity is less than 0.05, it indicates strong data validity. Additionally, a Cronbach's alpha coefficient in the range of 0.7 to 0.95 indicates strong scale reliability [10].  
After confirming the reliability and validity of the questionnaire, the data were processed using Exploratory Factor Analysis (EFA). Common factors with eigenvalues (\(\lambda\)) greater than 1 were retained [50]. These common factors and their included usability indicators were then grouped and named accordingly.