## 3 Methods  
### Service Quality Risk Evaluation  
Based on the methods described above, this study developed a complete conceptual framework incorporating [PERSON]'s model, an IPA, and an FMEA quality risk assessment model to measure the bus service quality risk. The following three steps were designed to develop a quality risk assessment model. Step 1 was to classify service quality characteristics based on [PERSON]'s model. Step 2 combined [PERSON]'s model and an IPA into a contributory improvement index (CII). Step 3 was to calculate Cr by combining the CII and the FMEA. * Step 1: Development of Composite Satisfaction Index (CSI)  
Based on the literature review above, [PERSON] et al. (1993) proposed two useful customer satisfaction coefficient indices: the satisfaction increment index (SII) and the dissatisfaction increment index (DDI) according to the results of [PERSON]'s model. This indicates that product or service features may affect customer satisfaction. In other words, the attribute has both the SII and DDI effects, although these two effects have no sub-relationship with compliance or non-compliance. [PERSON] and [PERSON] [60] pointed out that the SII shows an increase in satisfaction, while the DDI shows a decrease in satisfaction. Therefore, the combined effect of these two indicators can be regarded as the overall change in satisfaction to understand the influence of specific attributes on satisfaction. According to the customer satisfaction coefficient matrix proposed by [PERSON] et al. (2018), the vector concept is used to combine the SII and DDI. The equation for the CSI by [PERSON] and [PERSON] (2018) is as follows:  
\[\text{CSI}=\sqrt{\text{SII}^{2}+\text{DDI}^{2}}/\sqrt{2} \tag{3}\]  
To compare the degree of satisfaction between different quality attributes, [PERSON] and [PERSON] (2018) suggested that the distance between the attribute and the origin can be used as the comparison value. In the above, the coordinates of the two axes of SII and DDI are regarded as two different vectors, and the two vertical vectors are added together to reflect the design concept of the CSI system to understand the degree of comprehensive improvement of satisfaction with attributes. * Step 2: Contributory Improvement Index (CII)  
Considering the importance of research on satisfaction levels and the characteristics of improvement to meet customer satisfaction, this study's objective was to measure the quality of service performed using an IPA and the CSI. The results will show how satisfied customers were with the services and can be used as assessments to improve service quality. This concept was useful in calculating the standard weights, which contained the satisfaction and importance results and could indicate the priority of improvement from the IPA. The importance and satisfaction of each attribute were measured via a questionnaire with a five-point Likert scale. The processes to calculate the standardized weight were as follows. * Calculate the difference index, which equals importance minus satisfaction. * Rank the difference index from maximum to minimum. If there are some attributes with the same difference index, the attribute with better satisfaction has a higher ranking. This ranking number is called the priority weight (\(\text{w}_{\text{i}}\)) which represents the attribute with a larger value of difference index ranking priors to be improved. * Calculate the standardized weight (\(\text{a}_{\text{i}}\)) via normalizing the priority weight. The equation is:  
\[\text{a}_{\text{i}}=\text{w}_{\text{i}}/\text{max}(\text{w}_{\text{i}}) \tag{4}\]  
To obtain a more comprehensive understanding of the relative importance of both standard weights and the customer satisfaction index (CSI), this study proposed a synthetic index called the contributory improvement index (CII).