## 5 Result Analysis
Analysis of the Effect of Psychographic Segmentation on Predicting and Explaining E-Commerce User Preferences  
To investigate our three main questions, we further compare the differences between the SVS, BFF, and random segmentation, and their subdimensions, in predicting and explaining consumer preferences, while excluding the influence of other experimental settings. 2.1 Q1: Analysis of the Effect of the Clustering-Based Segmentation in Predicting and Explaining User Preferences  
We conduct [PERSON]'s range test, which is a single-step multiple comparison procedure and statistical test, on Table 4. Table 6 shows the [PERSON]'s range test results for the preferences based on the psychographic variables and DBSCAN segmentation method. Surprisingly, we can see that there is no significant difference in both RMSE and R\({}^{2}\), regardless of whether we use DBSCAN to obtain psychographic segments. Additionally, the psychographic variables significantly improve the performance of user preference explanation (R\({}^{2}\)), whereas the improvement of efficiency is not significant in user preference prediction (RMSE). 2.2 Q2: Analysis of the Significance and Differences between Psychographic Tools in Predicting and Explaining User Preferences  
We applied [PERSON]'s range test to further compare the differences between the SVS, BFF, and random segmentation within psychographic variables. Table 7 shows the results. For all product categories that we study, there are no significant differences between the three groups in terms of RMSE. By contrast, there are significant differences between both the SVS and BFF with the random segmentations in R\({}^{2}\) \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**[PERSON]’s Test for Segmentation**} \\ \hline
**Evaluations** & **G 1** & **G 2** & **Lower** & **Meandiff (G2-G1)** & **Upper** & **Reject (\(p\) \(<\) 0.05)** \\ \hline R\({}^{2}\) & Clustered by DBSCAN & Not Clustered by DBSCAN & \(-\)0.05 & \(-\)0.0027 & 0.0446 & False \\ \hline R\({}^{2}\) & Clustered by DBSCAN & Random & \(-\)0.167 & \(-\)0.1201 & \(-\)0.0732 & True \\ \hline R\({}^{2}\) & Not Clustered by DBSCAN & Random & \(-\)0.1721 & \(-\)0.1174 & \(-\)0.0626 & True \\ \hline RMSE & Clustered by DBSCAN & Not Clustered by DBSCAN & \(-\)0.0623 & \(-\)0.011 & 0.0403 & False \\ \hline RMSE & Clustered by DBSCAN & Random & \(-\)0.0103 & 0.0406 & 0.0914 & False \\ \hline RMSE & Not Clustered by DBSCAN & Random & \(-\)0.0078 & 0.0516 & 0.1109 & False \\ \hline \hline \end{tabular}
\end{table} 
Table 6: [PERSON]’s range test for psychographic segmentation as a whole in understanding consumer preference. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Evaluation** & **F** & **PR (\(\succ\)F)** & **df** & **Sum\_sq** \\ \hline Psychographic & R\({}^{2}\) & 36.39 & 0.0000 & 2 & 0.4120 \\ Regression Methods & R\({}^{2}\) & 32.55 & 0.0000 & 3 & 0.5526 \\ Product Category & R\({}^{2}\) & 7.44 & 0.0000 & 4 & 0.1684 \\ Residual & R\({}^{2}\) & & & 147 & 0.8320 \\ Clustering or Not & R\({}^{2}\) & 35.20 & 0.0000 & 2 & 0.4028 \\ Psychographic & RMSE & 9.93 & 0.0001 & 2 & 0.0516 \\ Clustering or Not & RMSE & 13.17 & 0.0000 & 2 & 0.0659 \\ Regression Methods & RMSE & 102.35 & 0.0000 & 3 & 0.7677 \\ Product Category & RMSE & 76.31 & 0.0000 & 4 & 0.7632 \\ Residual & RMSE & & & 147 & 0.3676 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA results for experimental settings in predicting consumer rating. We plot the mean values of RMSE and R\({}^{2}\) for the three segmentation variables in the different product categories; Figure 5 shows the results. For differences between the SVS and BFF in user rating explanation, the SVS is superior to BFF in explaining consumer preferences (R\({}^{2}\)) across all product categories, except for Toys and Games. For user rating prediction, the SVS and BFF tend to perform better than random segmentation in predicting user preferences (RMSE) in Beauty, Digital Music, and Office Products.