## 3 Results
### Mediation by Cognitive Fluency  
As shown in Table 5, we tested the mediation effect with the Bootstrap sampling technique and used PROCESS model 7 on 5000 replicates. First, we set the Ci + Ct in the design type as the virtualized coding benchmark, and the results show that the design type (Ci + At, Ai + Ct, Ai + At) has a significant direct impact on the response time (\(\beta\) = 1.416, Boot-SE = 0.079, \(p<0.05\); \(\beta\) = 1.453, Boot-SE = 0.086, \(p<0.05\); \(\beta\) = 1.703, Boot-SE = 0.107, \(p<0.05\)) of 95% of the three tests. The confidence intervals are in the ranges (1.272, 1.554), (1.349, 1.570) and (1.520, 1.895), respectively. Secondly, the construal level feature has a moderating effect on some design types (Ai + Ct and Ai + At) and cognitive fluency (\(\beta\) = 0.899, Boot-SE = 0.192, \(p<0.05\); \(\beta\) = 1.123, Boot-SE=, 0.0224, \(p<0.05\)), and the 95% confidence intervals of the two tests are in the ranges of (0.520, 1.267) and (0.681, 1.569), respectively. It is not surprising that the construal level feature has a moderating effect on the design type (Ci + At) and cognitive fluency (\(\beta\) = 0.184, Boot-SE = 0.152, \(p>0.05\)). The 95% confidence interval is in the range (\(-0.108\), 0.487). Figure 5: Match rate for four types of combination design icons in two groups of users with different construal levels. Finally, the results show that cognitive fluency mediates the interactive effect of some design types (Ai + Ct, Ai + At) and explanation level on the response time, and has a significant impact (\(\beta=-0.303\), Boot-SE = 0.076, \(p<0.05\); \(\beta=-0.378\), Boot-SE = 0.096, \(p<0.05\)). The 95% confidence intervals of the two tests were in the ranges (\(-0.455\), \(-0.163\)) and (\(-0.584\), \(-0.199\)), respectively. Cognitive fluency does not clearly mediate the interaction effect between design type (Ci + At) and construal level on response time (\(\beta=-0.062\), Boot-SE = 0.053, \(p>0.05\)). The 95% confidence interval is in the range (\(-0.172\), \(0.035\)). The mediation model is shown in Figure 6.  
As shown in Table 6, we also used Ci + Ct in the design type as the virtualized coding benchmark. The results show that the design type (Ci + At, Ai + Ct, Ai + At) has a significant direct impact on the matching rate (\(\beta=-0.682\), Boot-SE = 0.047, \(p<0.05\); \(\beta=-1.053\), Boot-SE = 0.052, \(p<0.05\); \(\beta=-1.898\), Boot-SE = 0.064, \(p<0.05\)), and the 95% confidence intervals of the three tests are in the ranges (\(-0.732\), \(-0.632\)), (\(-1.172\), \(-0.944\)) and (\(-2.055\), \(-1.761\)), respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Direct Effect of Combination Design Type** & & \\ \hline  & **Coeff** & **Boot-S.E** & **BootLLCI** & **BootULCI** \\ \hline Constant & \(-1.143\) & \(0.033\) & \(-1.207\) & \(-1.079\) \\ Ci + At & \(1.416\) & \(0.071\) & \(1.272\) & \(1.554\) \\ Ai + Ct & \(1.453\) & \(0.056\) & \(1.349\) & \(1.570\) \\ Ai + At & \(1.703\) & \(0.096\) & \(1.520\) & \(1.895\) \\ \hline \hline \multicolumn{5}{c}{**Indirect Effect of Combination Design Type \(\times\) Construal Level**} \\ \hline  & **Coeff** & **Boot-S.E** & **BootLLCI** & **BootULCI** \\ \hline Ci + At & \(-0.062\) & \(0.053\) & \(-0.172\) & \(0.035\) \\ Ai + Ct & \(-0.303\) & \(0.076\) & \(-0.455\) & \(-0.163\) \\ Ai + At & \(-0.378\) & \(0.096\) & \(-0.584\) & \(-0.199\) \\ \hline \hline \multicolumn{5}{c}{**Index of Moderated Mediation**} \\ \hline  & **Index** & **Boot-S.E** & **BootLLCI** & **BootULCI** \\ \hline  & \(-0.337\) & \(0.037\) & \(-0.407\) & \(-0.264\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The mediating role of the effects of cognitive fluency in combination design style and construal level on response time. Figure 6: The mediating model of cognitive fluency in response time. Note: \({}^{*}p\leq 0.05\).