## 4 Research Method
### Service Effectiveness Evaluation Based on Fuzzy-FMEA  
According to the aforesaid study background and literature review, we found that the fuzzy-FMEA method should be employed in a phased manner to handle various issues in complex cultural emotional experience activities, taking into consideration both individual differences and multilevel experiential needs. We put the index system into the fuzzy-FMEA framework, transforming the evaluation standards into new risk assessment factors, which is useful for satisfying user demands at various levels. Secondary indicators are used as identification criteria for touchpoints and failure modes, and them derived from multiple target group interview data can successfully decrease user cognitive biases due to individual variations. Fuzzy language is utilized to analyze each risk mode's correlation (C), restoration (R), and identity (I), and touchpoint failure risk is prioritized. Improved approaches may successfully reduce subjective errors while increasing assessment objectivity. Figure 3 illustrates the assessment processes for the methods provided in this study. (1) Build an experts group. Risk assessment is a process involving expert judgment, and it is critical to reduce expert uncertainty and cognitive bias. To achieve this goal, stakeholders in the CCPs service process are brought together, and expert evaluation teams are formed. Using their extensive historical knowledge and skills, they analyze the touchpoints and failure modes that occur  
Figure 3: Efficiency assessment processes of symbolic CCPs based on fuzzy-FMEA. throughout the judging process, determine the risk level, and design assessment programs in collaboration. Experts' knowledge and abilities vary significantly. Assuming there are \(m\) experts, \(j=1,2,\ldots,m\), \(a_{Cj}\), \(a_{Rj}\), and \(a_{lj}\) are the evaluated weighting values of the correlation, restoration, and identity from \(j\)th experts for failure modes. Build a fuzzy term set and determine the degree of membership function. The article defines a fuzzy term set using the new risk assessment factors correlation (C), restoration (R), and identity (I). Each factor is an integer between 1 and 10, and Table 4 shows the meanings of the associated fuzzy terminology. In this study, triangular fuzzy numbers are employed to define terminology sets, with \(\tilde{A}=(L,M,U)\) indicating a matching triangular fuzzy number for each fuzzy terminology. The membership function measures the risk factor's degree of dependency, and its value range is (0, 1), with closer to 1 indicating a larger degree of mapping. The membership function curves refer to Figure 2 in the preceding text. The expert group assesses three failure risk factors. During this step, each expert is asked to assess the failure model's three risk factors. Assume there are \(n\) touchpoints (\(T_{i}\), \(i=1\), 2, \(\ldots\), \(n\)). \(C_{ij}\), \(R_{ij}\), and \(I_{ij}\) are the ratings of the \(j\)th experts on the correlation, extension, and identity of the \(i\)th touchpoint. \(C_{i}\), \(R_{i}\), and \(I_{i}\) are values describing the fuzzy numbers of correlation, extension, and identity for the \(i\)th failure touchpoint, whose calculation is based on Formulas (4)-(6). \[C_{i}=\sum_{j=1}^{m}a_{Cj}C_{ij}=\left(\sum_{j=1}^{m}a_{Cj}C_{ijL},\sum_{j=1}^ {m}a_{Cj}C_{ijM},\sum_{j=1}^{m}a_{Cj}C_{ijU}\right) \tag{4}\]  
\[R_{i}=\sum_{j=1}^{m}a_{Rj}R_{ij}=\left(\sum_{j=1}^{m}a_{Rj}R_{ijL},\sum_{j=1}^ {m}a_{Rj}R_{ijM},\sum_{j=1}^{m}a_{Rj}R_{ijU}\right) \tag{5}\]  
\[I_{i}=\sum_{j=1}^{m}a_{lj}I_{ij}=\left(\sum_{j=1}^{m}a_{lj}I_{ijL},\sum_{j=1} ^{m}a_{lj}I_{ijM},\sum_{j=1}^{m}a_{lj}I_{ijU}\right) \tag{6}\]  
In the formula, each fuzzy level has three fuzzy numbers, which are the minimal fuzzy number of the \(j\)th experts on the correlation, extension, and identity of the \(i\)th touchpoint. Similarly, M represents the middle fuzzy number, and U represents the maximum fuzzy number.