# Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption
## 4 Results  
### Estimating Correlations between Variables Using MIMIC  
#### 4.1.1 The Correlation between Attitude Latent Variables  
The EFA is first conducted to explore the proper groups of latent factors that explain all the original statement variables. The [PERSON]'s Scree Plot method is used to decide the number of factors. Based on the preliminary EFA, the CFA extracted 10 latent attitudinal factors from 32 statement variables using AMOS 24.0. The CFA goodness of fit index (GFI) is 0.953, indicating that the given model can reproduce 95.3% of the co-variation in the data. The CFA shows the ten-dimension latent factor structure is appropriate for the data of this study. Based on prior information on the correlation structures from the CFA in this study and several studies [38; 39; 40], the model structure shown in Figure 3 is specified in the MIMIC model. The measurement model and structural model are simultaneously estimated. Figure 3 also shows the model estimates the correlations between the latent factors. The goodness of fit index (GFI) of the MIMIC model is 0.953, the root mean square residual (RMR) is 0.028, and the root mean square error of approximation (RMSEA) is 0.012, indicating that these measures meet the acceptable criteria. The incremental fit index (IFI) = 0.925 and comparative fit index (CFI) = 0.917 exceed the ideal value of 0.9. The adjusted goodness of fit index (AGFI) = 0.944 is above the recommended value of 0.8. These indices indicate that the final MIMIC model is a good fit. As shown in Figure 3, several attitudinal factors are significantly related to travelers' use willingness towards EVTRs. Travel obstacles (including station inconvenience, station  
Figure 3: Path relationship between latent attitudinal variables in the MIMIC model. inconvenience, use inconvenience, and personal inconvenience) have a negative impact on the perception towards EVTRs. The other attitude latent variables have a positive impact on the perception towards EVTRs, as follows:  
* The coefficient between use preference and use willingness in the MIMIC model is 0.745, indicating that use preference is positively related to use willingness, which proves that Hypothesis 1 is true;
* The coefficient between perceived usefulness and use willingness in the MIMIC model is positive (perceived comfort 0.512 and perceived efficiency 0.593), indicating that perceived usefulness is positively related to use willingness, which proves that Hypothesis 2 is true;
* The coefficient between travel obstacles and use willingness in the MIMIC model is negative (station inconvenience \(-\)0.393, station inconvenience \(-\)0.405, use inconvenience \(-\)0.501, and personal inconvenience \(-\)0.439), indicating that perceived availability is positively related to use willingness, which proves that Hypothesis 3 is true;
* The coefficient between subject norm and use willingness in the MIMIC model is 0.121, indicating that subject norm is positively related to use willingness, which proves that Hypothesis 4 is true;
* The coefficient between subjective evaluation and use willingness in the MIMIC model is 0.878, indicating that subjective evaluation is positively related to use willingness, which proves that Hypothesis 5 is true. The travelers' perception towards EVTRs has a significant positive impact on their willingness to use EVTRs. Other atttitudinal factors are also related to this willingness. To further study the relationship between latent variables, we examined the mediating effect among potential variables in three steps through the mediating effect test method summarized by [PERSON].