## 3 Empirical Research
### Analysis of Results
#### 3.3.2 Analysis of the Results of the Two-Stage Dynamic Network Model  
Using the two-stage dynamic network model in Section 2.2 to assess the 30 provinces, it is able to obtain the overall efficiency value, the stage-one efficiency value, and the stage-two efficiency value (Table 7). The model takes into account the two-stage relationship of the technological innovation process in China's high-tech manufacturing industry and  
Figure 3: Annual efficiency comparison according to four DEA models. introduces a time dimension, which makes the assessment of the innovation efficiency of each province more comprehensive and dynamic. By dividing the innovation process into two stages: technology development and achievement transformation and setting different lags in the model, the efficiency differences between the stages can be visualized. This design enables the model to reveal more precisely the innovation performance of each stage at different points in time, helping to analyze the bottlenecks and potential advantages of a particular stage within a certain time period. In addition, the introduction of the time dimension helps to better grasp the lag effect of innovation activities, thus providing more specific decision support for policymakers and promoting the positive interaction between technological innovation and achievement transformation. According to Table 7, it can be seen that about half of the provinces are more efficient at the technology R&D stage than at the transformation stage. This suggests that in these provinces, the input and output efficiencies of innovative technology R&D are more prominent, while there may be certain bottlenecks or inefficiencies in the transformation stage, resulting in a relatively weaker ability to transform their results. However, although the dynamic network DEA model has improved over the traditional self-assessment model in some aspects, it still retains some of the limitations of the traditional DEA model, especially in assessing efficient decision-making units. Specifically, a number of provinces, including Beijing, Ningxia, Shanxi, Jiangxi, Henan, Chongqing, and Qinghai, have an overall efficiency value of 1 and are assessed as \"optimally efficient\" units in the model; the \begin{table}
\begin{tabular}{c c c c c} \hline
**Serial Number** & **DMU** & **Overall Efficiency Value** & **Stage 1 Efficiency Value** & **Stage 2 Efficiency Value** \\ \hline
1 & Shanghai & 0.774405 & 0.443462 & 0.146154 \\
2 & Yunnan & 0.947053 & 0.332505 & 0.448106 \\
3 & Neimenggu & 0.863417 & 0.495243 & 0.325395 \\
4 & Beijing & 1 & 0.590699 & 0.612533 \\
5 & Jilin & 0.758956 & 0.446096 & 0.200793 \\
6 & Sichuan & 0.802218 & 0.486668 & 0.279002 \\
7 & Tianjin & 0.928797 & 0.302415 & 0.429519 \\
8 & Ningxia & 1 & 0.318312 & 0.753214 \\
9 & Anhui & 0.990402 & 0.604228 & 0.487683 \\
10 & Shandong & 0.921920 & 0.364952 & 0.418672 \\
11 & Shanxi & 1 & 0.168742 & 0.548059 \\
12 & Guangdong & 0.856469 & 0.570199 & 0.053439 \\
13 & Guangxi & 0.975229 & 0.606961 & 0.467347 \\
14 & Xinjiang & 0.764179 & 0.510609 & 0.146037 \\
15 & Jiangsu & 0.931260 & 0.393364 & 0.422927 \\
16 & Jiangxi & 1 & 0.441619 & 0.547284 \\
17 & Hebei & 0.866948 & 0.308851 & 0.366886 \\
18 & Henan & 1 & 0.274016 & 0.910339 \\
19 & Zhejiang & 0.959041 & 0.383756 & 0.456063 \\
20 & Hainan & 0.682115 & 0.438424 & 0.048969 \\
21 & Hubei & 0.847507 & 0.581689 & 0.204838 \\
22 & Hunan & 0.878838 & 0.360040 & 0.377812 \\
23 & Gansu & 0.783275 & 0.281361 & 0.283223 \\
24 & Fujian & 0.875199 & 0.384368 & 0.372973 \\
25 & Guizhou & 0.728923 & 0.405509 & 0.161186 \\
26 & Liaoning & 0.747690 & 0.406133 & 0.230784 \\
27 & Chongqing & 1.00000 & 0.420755 & 0.537713 \\
28 & Shaanxi & 0.696487 & 0.233117 & 0.193475 \\
29 & Qinghai & 1 & 0.628615 & 0.728512 \\
30 & Heilongjiang & 0.845018 & 0.243439 & 0.345608 \\ \hline \end{tabular}
\end{table} 
Table 7: Overall efficiency results of the two-stage dynamic network. reason might be that the model failed to fully identify their potential inefficiency problems during the evaluation process.