## 5 Discussion
The participants were asked  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Failure Mode No. **} & \multicolumn{3}{c}{**RPN Derived from the Traditional FMEA Method**} & \multicolumn{3}{c}{**FRPN Derived from the**} \\ \cline{2-6}  & **Occurrence** & **Severity** & **Detection** & **RPN** & **Proposed Approach** \\ \hline
1 & 8 & 7 & 4 & 224 & 6.395 \\
16 & 7 & 8 & 4 & 224 & 6.384 \\
8 & 9 & 8 & 3 & 216 & 6.432 \\
20 & 8 & 9 & 3 & 216 & 6.823 \\
11 & 7 & 6 & 5 & 210 & 6.374 \\
12 & 7 & 5 & 6 & 210 & 6.289 \\
23 & 6 & 7 & 5 & 210 to execute the assigned tasks and then assess both versions of the IVIS using SUS. The revised IVIS received a SUS score of 90.12. This was a significantly higher score compared to that of the original IVIS, which only garnered a score of 60.15. The statistical significance of this difference was confirmed (\(p<0.05\)). These results suggest that the recommended measures that were incorporated into the revised IVIS design were indeed effective, and the revised design has proven to be more usable. Therefore, these findings serve to validate the proposed approach, demonstrating its effectiveness in enhancing the UX of the IVIS. This study primarily focused on identifying and ranking failure modes. Despite the fact that the causes of these failure modes were delineated with clarity, we did not delve into a comprehensive investigation of the underlying mechanisms of these causes. To facilitate a deeper understanding of these mechanisms, one potential approach could involve the application of fault tree analysis, which is often utilized in the exploration of potential causes for failure modes [66; 67]. Future research could concentrate on integrating fault tree analysis with the approach proposed in this paper to research the root causes of failure models concerning UX [68]. Note that in this case study, failure mode identification was carried out in a stationary vehicle, not a moving one. This had a minimal impact on the accuracy of error identification results [69]. Future studies could expand this research to include vehicles in motion on highways. Our approach is slightly complex and comprises several computational steps, but it is properly carried out. More importantly, it is composed of four simplification stages, namely, detecting failure modes, analyzing failure modes, calculating FRPNs by using fuzzy logic, and modifying the failure modes with high priorities. The fuzzy calculation process can be facilitated through programming. In this study, the programming was implemented using the fuzzy logic toolbox for MATLAB. It involved aggregating fuzzy opinions for risk parameter assessments and calculating FRPNs with fuzzy logic. The proposed approach will have broad applications due to these four simplification stages. While this study employed an IVIS as an example, the approach outlined in this research is universal and can be applied broadly to other interactive systems, including mobile apps, software interfaces, and even specific hardware controls, with the aim of improving UX. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Failure Mode No. ** & **Prioritization Based on the Proposed Approach** & **Prioritization Based on the Combination of FMEA with Fuzzy TOPSIS** & **Prioritization Based on the Combination of FMEA with Fuzzy Logic** \\ \hline
1 & 8 & 9 & 9 \\
2 & 19 & 16 & 19 \\
3 & 1 & 2 & 1 \\
4 & 14 & 18 & 11 \\
5 & 15 & 15 & 11 \\
6 & 25 & 25 & 25 \\
7 & 12 & 12 & 11 \\
8 & 6 & 4 & 5 \\
9 & 20  
Possible future works include identifying automated repair methods for the failure modes identified and ranked through the proposed method, identifying how the FRPN values from the proposed method can help with such repair, and adding the fuzziness of human reasoning while interacting with the system.