## 4 Results and Discussion  
### Reliability and Validity Tests of the Scale  
In this study, SPSS 26.0 software was used to test the reliability of the questionnaire. The reliability test results of the variables in this study are shown in Table 4. The reliability test revealed that the [PERSON]'s alpha values corresponding to the four dimensions of this study were all greater than 0.7 and were 0.926, 0.881, 0.938, and 0.947, respectively, indicating good internal consistency among all the dimensions of the questionnaire. After the deletion of ITB1, the [PERSON]'s alpha value was 0.911, which was greater than the [PERSON]'s alpha value of ITB1 (0.881), and the CITC value was low. After IBC3 was deleted, the [PERSON]'s alpha value was 0.944, which was greater than the [PERSON]'s alpha value for IBC3 (0.938). Therefore, the items were screened, ITB1 and IBC3 were deleted, and the remaining 21 items were ultimately eliminated. The two indexes of \"intelligent business process integration\" and \"intelligent IP management\" in the corresponding evaluation index system were also eliminated. After the first reliability test, adjustments were made and the second reliability test was carried out using the modified scale. The results of the second reliability test are shown in Table 5.  
In previous studies, validity was described from four main aspects, namely, content validity, structural validity, discriminative validity, and aggregate validity. First, the questionnaire design of this study was based on the premise of combining the existing relevant research and referring to the relevant data in grounded theory, and the design was recognized by industry experts. The questionnaire was revised based on expert opinion, thus ensuring the content validity of the scale. Measuring structural validity is usually performed using factor analysis. In this study, exploratory factor analysis and confirmatory factor analysis were used to test the structural validity of the scale. In this study, SPSS 26.0 software was used to perform the KMO test and Bartlett sphericity test on the sample data. The KMO value of the scale was 0.870, which is greater than 0.7, and the Bartlett sphericity test chi-square was significant (\(p<0.001\)), indicating that the scale data met the factor analysis conditions. Principal component analysis was used to extract factors in the form of feature roots greater than 1. As shown in Table 6, there were four factors with eigenvalues greater than 1; the cumulative variance explanation rate of these four factors after rotation was 81.566%, which is greater than 60%, and the information expression met the statistical requirements. Through the orthogonal rotation of the factor load matrix using the maximum variance method, combined with the factor load coefficient, the research scale in this section had a total of 21 items, which were divided into four factors, and the composition of the four factors was consistent with the theoretical construction of the research design. As shown in Table 7, the absolute value of the factor load corresponding to all the items in the scale was greater than 0.5. This shows that the scale has high structural validity. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**Initial Eigenvalue**} & \multicolumn{4}{c}{**Sum of the Squared Loads Extracted**} & \multicolumn{4}{c}{**Load Sum of Squares Rotated**} \\ \cline{2-10}  & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Variance\%**} & \multicolumn{2}{c}{**Accumulate**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Variance\%**} & \multirow{2}{*}{**Accumulate**} & \multirow{2}{*}{**\%**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Variance\%**} & \multirow{2}{*}{**Accumulate**} \\  & & **Variance\%** & & & & & & \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 6: Total variance explained.