## 4 Case Validation and Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Kanei** & **Frequency** & **Kanei** & **Frequency** & **Kanei** & **Frequency** & **Kanei** & **Frequency** & **Kanei** & **Frequency** \\
**Word** & **Frequency** & **Word** & **Frequency** & **Word** & **Frequency** & **Word** & **Frequency** \\ \hline good & 4786 & small & 351 & enthusiastic & 187 & enough & 100 & free & 65 \\ electric & 1137 & long & 342 & effective & 177 & light & 98 & useful & 63 \\ appearance & 1077 & performance & 321 & trustworthy & 169 & smooth & 97 & careful & 61 \\ fast & 1023 & pretty & 281 & particularly & 167 & attractive & 96 & cheap & 60 \\ beautiful & 851 & excellent & 280 & patient & 151 & endurance & 95 & sturdy & 60 \\ quality & 816 & strong & 270 & sufficient & 135 & positive & 91 & pop & 57 \\ high & 687 & stable & 236 & intelligent & 127 & right & 80 & happy & 56 \\ speed & 647 & suitable & 235 & simple & 122 & professional & 79 & expected & 54 \\ satisfied & 645 & shock & 231 & durable & 120 & decent & 77 & amazing & 54 \\ convenient & 528 & big & 216 & perfect & 112 & cool & 75 & national & 53 \\ easy & 518 & lightweight & 213 & helped & 109 & friendly & 74 & forward & 53 \\ experience & 513 & new & 211 & compact & 109 & fashionable & 72 & safe & 52 \\ powerful & 498 & affordable & 207 & exquisite & 108 & satisfying & 72 & & \\ comfortable & 444 & large & 188 & cute & 105 & practical & 69 & & \\ worth & 423 & awesome & 187 & handsome & 103 & stylish & 68 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Seventy-two Kansei words and their frequency. Figure 12: Three selected products. of sphericity value was \(p=0.000\), indicating the data was spherically distributed, which means that the variables were not independently distributed and were suitable for FA. FA was conducted on the questionnaire data for the clustering of Kansei words. When three clustering factors were selected, the cumulative variance interpretation rate was 72.105%; thus, the factors were extracted into 3. The maximum variance method was applied to the factors to obtain the rotated factor matrix, as shown in Table 4. Absolute values of factor loading coefficients below 0.5 were omitted from the display in the table for clarity. Factor 1 consists of 4 Kansei words: \"lightweight\", \"compact\", \"cute\", and \"fashionable\", which name the aesthetic factor. Factor 2 consists of \"beautiful\", \"convenient\", and \"comfortable\", which name the affinity factor. Factor 3 consists of \"stable\", \"durable\", and \"safe\", which name the trust factor. _Step 5: Calculating Kansei CRs weights._ Grey theory was integrated into the AHP to construct a 3-layer Kansei CR model, with females' Kansei CRs for E-mopeds as the target layer, 3 Kansei factors as the criterion layer, and 10 Kansei words as the sub-criterion layer. Twelve experts were invited to make judgments on perceptual demands, including 2 design directors with more than 8 years of industrial design experience, 3 industrial design professors, and 7 master's degree graduate students majoring in industrial design, with 50% being female. Experts conducted a questionnaire interview in the form of focus groups to obtain the two-by-two pairwise comparison matrix for each expert's judgment on the criteria and sub-criteria. Taking expert 1 as an example, judgment matrix \(A_{1}\) was obtained using Equations (1) and (2), and interval grey numbers were used to value the judgment matrix. \[A_{1}= \begin{pmatrix}[1,1]&[2,4]&[4,6]\\ [1/4,1/2]&[1,1]&[2,4]\\ [1/6,1/4]&[1/4,1/2]&[1,1]\end{pmatrix} \]  
The judgment results from the 12 experts were calculated using Equations (3) and (4) to obtain the main pairwise comparison matrix and weights, as shown in Table 5.  
According to Equations (5)-(8), the judgment results of the sub-criterion layer were calculated in sequence, and the initial grey weight results of each of the Kansei words are shown in Tables 6-8.